Large language models (LLMs) are becoming better at handling larger context windows. But does that mean retrieval-augmented generation (RAG) is obsolete? Not quite. 

Here’s a quick look at what this guide covers:

* How RAG delivers grounded and user-friendly responses through retrieval and generation. 
* The hype surrounding long-context LLM models like Gemini 1.5 Pro or GPT-4.1, which can process thousands of tokens simultaneously.
* The major difference between RAG and long-context as well as the gap between them.
* When to use RAG and long-context LLMs, and what makes one better than the other in different situations. 
* A look at the pros and cons of each, and whether there is a need to use both together.
* How both RAG and long-context LLMs perform in real-world use cases, from AI agents to enterprise knowledge bases.

Let’s find out which approach gives you better, more grounded responses while incurring minimal costs.

## What is retrieval-augmented generation (RAG)?

[Retrieval-augmented generation](https://www.meilisearch.com/blog/what-is-rag) connects information retrieval with LLMs to generate accurate responses. How? By searching external knowledge bases or vector bases for relevant documents. 

RAG provides an LLM with retrieved chunks of external data, providing relevant context and helping it produce the response the user is looking for. 

As a result, RAG reduces the likelihood of AI hallucinations. It’s used in [AI agents](https://www.meilisearch.com/blog/agentic-rag) and real-time workflows that depend on external data.

Next, let’s take a look at how long-context LLMs handle larger context windows without retrieval.

## What is a long-context LLM?

A long-context LLM, as the name suggests, is a large language model specifically designed to handle a much larger context window size than the standard models. The difference is quite staggering, as it can sometimes process even millions of tokens without relying on retrieval.

Modern examples include GPT-4.1, Gemini 2.5 Pro, Claude 4 Opus, and Llama 4 Maverick. They can read entire reports, research papers, or knowledge bases at once, making them ideal for summarization, deep reasoning tasks, and extended document analysis.

Next, let’s compare how these long-context models differ from RAG.

## How does RAG differ from long-context LLMs?

The basic difference you need to understand between RAG and long-context LLMs is that they handle context differently. 

RAG retrieves relevant information from external sources before generation. Long-context LLMs, on the other hand, process all input within a single, long-context window. 

![RAG vs Long-Context LLMs.png](https://unable-actionable-car.media.strapiapp.com/RAG_vs_Long_Context_LL_Ms_cbc72da795.png)

While accuracy and scalability are goals for both approaches, the methods used to achieve them differ. 

If you’re exploring different architectures, our post on [RAG types](https://www.meilisearch.com/blog/rag-types) breaks down each variant with examples. 

Now, let’s see when using one of these is better than the other.

## When should you use RAG over long-context LLMs?

Retrieval-augmented generation is the perfect match when your app relies on external knowledge or dynamic datasets. If you need real-time retrieval and scalability, you can’t go wrong with RAG.

RAG handles changing knowledge bases and keeps latency predictable, minimizing the required number of tokens per query. 

It’s cost-effective and enables domain-specific optimization as well as granular access control for transparent audit trails. 

## When are long-context LLMs a better choice?

The best time to use long-context LLMs is when your data fits inside a single large context window and doesn’t change often. Summarization, reasoning, technical reports, understanding transcripts; they do it all. 

The newer models, such as GPT-4.1, Gemini 2.5 Pro, Claude 4 Opus, and Llama 4 Maverick, push context window sizes past a million tokens and easily handle complex cross-document reasoning. Additionally, these models can handle entire datasets directly, eliminating the need for retrieval pipelines.

As amazing as that sounds, it comes with a significant computational cost. The latency also goes up for larger inputs. 

In short, for static knowledge bases, more extended context exploration, or quick experimentation, long-context LLMs are often the more practical path.

## What are the main advantages of RAG?

RAG gives you all the control and flexibility you need to build faster and minimize costs when developing your system. 

Let’s see what makes RAG special:

1. **Efficient use of resources:** By retrieving only relevant documents, RAG keeps the number of tokens small, reduces costs, and speeds up each query.
2. **Speed:** Thanks to the use of vector bases and embedding models in the retrieval process, the speed of RAG systems is unmatched.
3. **Flexibility and adaptability:** RAG easily integrates with various knowledge bases, requiring no fine-tuning.
4. **Full transparency:** Every answer can point back to its retrieved chunks, so it can easily be traced while maintaining user trust.

Want to see how teams optimize performance? Check out our official guide on [RAG tools](https://www.meilisearch.com/blog/rag-tools).

## What are the main advantages of long-context LLMs?

Long-context LLMs are by far the best option when you need the system to handle large inputs while retaining the context. 

Let’s take a deeper look at their pros:

1. **Thorough understanding:** These models process entire datasets or documents in a single pass, ensuring minimal fragmentation and preserving the meaning.
2. **Exhaustive reasoning:** Long-context LLMs shine in reasoning tasks and can connect ideas spread across long sequences for stronger logical flow.
3. **Simplicity:** There’s no need for external RAG pipelines or vector databases. Just input your text and generate.

Now, it’s time to see the limitations of both RAG and long-context LLMs.

## What are the limitations of RAG?

RAG is the go-to option for accuracy and control, but that doesn’t mean it comes without challenges. Let’s look at some below:

1. **Retrieval is not 100% accurate:** You may sometimes receive poor output quality due to inadequate chunking and embedding models.
2. **Costly maintenance:** Keeping indexes fresh requires ongoing updates to datasets and managing data pipelines. None of this is free or easy.
3. **Experienced prompt engineering skills needed:** RAG depends on well-structured prompts to merge retrieved chunks without confusing the LLM.
4. **Latency changes too easily:** Each retrieval step adds milliseconds to the response time. When you estimate this at scale, it adds up to a significant amount of latency. 

These factors make RAG more complex to deploy and maintain than long-context LLMs, which is why small teams tend to avoid it.

## What are the limitations of long-context LLMs?

Let’s now explore the key drawbacks to long-context LLMs:

1. **Excessive resource use:** When you want to process millions of tokens, you need more computational resources.
2. **Scaling isn’t easy:** It’s not straightforward to scale long-context LLMs, as latency and response times increase with added context length.
3. **Lost context:** You can lose important details, especially in extremely long inputs or mixed datasets.
4. **Data security:** The larger the prompt, the greater the risk of prompt injection, which can lead to data leakage and compromise enterprise security.

Even advanced models face these issues when pushed to their context window size limits.

Next, we’ll examine how both methods perform using real-world benchmarks.

## How do RAG and long-context LLMs perform on benchmarks?

Numerous qualified studies offer nuanced insights into how each approach performs. 

Let’s look at some of the most recent papers that discuss this in detail.

[‘Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach’](https://arxiv.org/abs/2407.16833) (Li et al., 2024) found that long‐context LLMs consistently outperformed RAG when ample resources were available, but RAG was undoubtedly far more cost-efficient.

Another study, [‘LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs – No Silver Bullet for LC or RAG Routing’](https://arxiv.org/abs/2502.09977) (Li et al., 2025), observed that there was no one-size-fits-all solution. The choice ultimately depends on the model size, task type, and other factors surrounding context length and retrieval quality.

Long-context models shine for long, static documents. On the other hand, RAG performs better when datasets are dynamic or diverse. 

## How do RAG and long-context LLMs impact costs?

It’s no secret that RAG offers lower overall costs for setup, while long-context LLMs incur a higher token and infrastructure cost at scale: 

* **Unique setup cost:** With RAG, the focus is on indexing, embedding, and retrieval. For LLMs, the focus is on a premium API or GPUs that support large context windows. 
* **Inference costs:** The inference cost for RAG is much lower since it only sends relevant chunks to the LLM. This way, it minimizes the token cost and usage. On the other hand, LLMs that are processing 200k+ tokens per request might even cost up to $20 per call. 
* **Scaling and maintenance costs:** RAG pipelines require ongoing index upkeep and retrieval tuning, but base LLM token consumption stays low. Long-context solutions scale poorly with more users or larger documents because each call incurs increased costs and latency.
* **Operational charges to consider:** Enterprises with strict cost or latency targets often choose RAG since it better suits their needs. For long-context LLMs, hidden costs like GPU usage, memory capacity, and slower responses become an uninvited guest that you don’t enjoy dealing with.

## What are real-world use cases for RAG?

RAG is ideal for domains that deal with rapidly changing contexts. These include:

1. **Enterprise knowledge systems:** Companies use RAG to consolidate all their internal data from wikis, emails, and CRMs into a unified knowledge base. AI assistants can then use this knowledge base to deliver relevant answers.
2. **Intelligent search engines:** RAG serves as the backbone for intelligent search engines. Thanks to vector databases and [hybrid retrieval](https://www.meilisearch.com/blog/hybrid-search), manual lookup time can be significantly reduced.
3. **Dynamic document management:** In fast-moving domains such as finance or healthcare, RAG enables real-time updates without requiring retraining of the AI model. This way, the output always reflects the latest updates.

## What are real-world use cases for long-context LLMs?

Long-context LLMs excel at handling tasks that require large inputs: 

1. **Summarization and analysis:** Models can process entire reports, transcripts, or books. They provide all the information you need from these sources, whether summaries, transcripts, or answers to questions that require reasoning.
2. **Legal and compliance review:** Long-context LLMs are a go-to choice for enterprises that need to review legal contracts in their entirety. They also help in flagging inaccurate info and comparing clauses without losing context.
3. **Academic and technical research:** Long-context systems can explore long datasets, scientific papers, or arXiv collections to identify relevant information and trends across different fields.

It’s now time to explain why LLMs’ growing capabilities are starting to challenge traditional RAG pipelines.

## Why are long-context LLMs challenging traditional RAG pipelines?

All these recent advances in context window size are reshaping how developers think about retrieval. 

Models that can handle over a million tokens reduce reliance on RAG pipelines and vector databases. For smaller or static datasets, long-context LLMs provide faster and simpler results without requiring any retrieval infrastructure. 

However, RAG remains undefeated when it comes to dynamic or real-time data retrieval, offering both data freshness and scalability.

Let’s see how to choose between these two approaches effectively.

## How to choose between RAG and long-context LLMs?

Choosing between RAG and long-context LLMs depends on your data volume, update frequency, and performance goals. Each approach can meet a distinct need:

1. **Project scope:** If the project is small or static, such as a policy chat or document summary, the long-context models are way easier to deploy. For larger projects that need real and ongoing reasoning, RAG is what you want.
2. **Data type and freshness:** If your system relies on constantly changing or real-time content (such as support tickets, logs, or news) RAG ensures the results remain current and up-to-date. 
3. **Scalability and cost:** RAG minimizes token usage, so it’s more cost-effective for enterprise-scale workflows. Long-context LLMs consume more tokens as the context length increases, resulting in increased latency and computational resources.
4. **Compliance and control:** When there can be no compromise with audit trails and explainability, such as in healthcare or finance, RAG’s indexed architecture provides audit-ready outputs. 

In short, RAG suits high-volume, dynamic ecosystems, while long-context LLMs excel in reasoning-intensive domains.

## How does retrieval work in RAG vs. long-context LLMs?

In RAG, retrieval happens before the model generates a reply. A retriever searches your knowledge base to find the most relevant documents, then passes the retrieved information to the large language model for response generation.

Tools like Meilisearch make this step even faster and production-ready. Meilisearch indexes content and performs hybrid retrieval (keyword + vector) with built-in filters, scoring, and reranking. This gives grounded and citable snippets that aren’t just accurate but also easy to track. It’s transparent and quick to update or scale through Meilisearch’s simple <code><em>/chat</em></code> API.

Long-context LLMs have no retrieval step at all. They rely entirely on what’s packed inside the prompt. 

While this works for short, static data, it becomes costly and less fresh as context windows grow. Additionally, there’s no automatic refresh or audit trails, so it is impossible to trace the origin of the response.

## Can RAG and long-context LLMs be combined?

Yes, and it’s often the best of both worlds. Hybrid setups use RAG to fetch relevant documents from a knowledge base, then feed them into a long-context LLM. Then comes the reasoning across the entire input combined.

This hybrid approach strikes the perfect balance between recall and reasoning. RAG ensures the prompts are lean and cost-effective, while the long-context LLMs handle the analysis and reasoning the user requires.

Meilisearch handles the retrieval layer in such systems: indexing, filtering, and returning grounded, high-quality snippets before the model processes them.

If you’re into experimenting with model correction or hybrid routing, explore [our guide on corrective RAG](https://www.meilisearch.com/blog/corrective-rag) for practical strategies.

## Bringing it together: choosing between RAG and long-context LLMs

Both RAG and long-context LLMs push the limits of how large language models use data, but their goals differ significantly from one another. 

RAG is all about accuracy and efficiency, but long-context LLMs excel in reasoning and retaining context. 

The best foundation you can build for any system is to combine both of them.

RAG vs. long-context LLMs: A side-by-side comparison

RAG (retrieval-augmented generation) indexing determines how well an AI system grounds its answers in relevant information. It enables the system to quickly locate relevant documents for a search query. 

Developing RAG indexing involves building a pipeline with the following steps: 

* Data collection 
* Meaningful chunking
* Metadata addition 
* Embedding
* Storage in a vector index

Good indexing reduces AI hallucinations and improves relevance across both structured and unstructured data.

RAG indexing strategies, such as hybrid, hierarchical, domain-specific, and graph-based, adapt to the structure of the knowledge base and the retrieval system's goals.

Issues caused by scale, chunk size, and metadata quality can be handled with clean data, consistent embeddings, and regular updates.

Factors such as speed, cost, integration needs, and dataset size are important considerations when selecting a database.

RAG indexing differs from keyword search by using embeddings rather than exact term matches.

Future RAG developments are moving toward hybrid retrieval methods, graph-enhanced approaches, and RAPTOR-style hierarchical indexing.

## What is RAG indexing?

RAG indexing is the process by which a [RAG system](https://www.meilisearch.com/blog/what-is-rag) organizes and prepares data so that, when prompted by a user query, the correct information can be easily retrieved. 

It takes the documents, chunks them into smaller pieces, creates vector representations of the chunks, and stores them in a special index so the system can quickly find them when needed. 

Without indexing, a RAG system would not know how to connect a user’s question to the relevant context. The index serves as a map of the knowledge base, enabling the retriever to quickly locate the most relevant text without having to search the entire knowledge base. 

That retrieved information is then passed to the LLM (large language model) to generate an appropriate answer to the query.

## How does RAG indexing work?

RAG indexing organizes the source data so that the RAG system can quickly find and return the most relevant information chunk. It converts raw documents into searchable vector embeddings and stores them in a vector database, enabling fast similarity searches. 

![How RAG Indexing Works.png](https://unable-actionable-car.media.strapiapp.com/How_RAG_Indexing_Works_2b7e28b7a3.png)

Here are the main stages of RAG indexing:

1. **Data collection:** RAG indexing begins by collecting data from various sources. These can be external PDFs, blog articles, an FAQ spreadsheet, etc. 
2. **Cleaning the text:** Next, the collected data is processed. Data cleaning may involve removing headers, fixing unparseable symbols, removing extra spaces, and so on. 
3. **Document chunking:** Long documents are typically split into smaller chunks to ensure the text is within the LLM’s context window. The chunks should be large enough to retain context but small enough to be processed efficiently. For instance, instead of treating a 40-page policy manual as one giant block of text, you can break it into hundreds of smaller sections.
4. **Tagging with metadata:** Next, each chunk is tagged with metadata for quick filtering. The metadata are relevant fields such as title, data source, topic, category, or date. For example, tagging a chunk from a refund policy document with ‘topic: refunds' allows the system to instantly find it when a user asks a related question.
5. **Embedding:** The chunks are then converted into [vector embeddings](https://www.meilisearch.com/blog/what-are-vector-embeddings) to ensure similar chunks are placed close to each other. For example, we can expect chunks on ‘refund rules’ and ‘return policy’ to be close because they would share similar vectors.
6. **Vector storage:** Finally, these vectors and their metadata tags are stored in a [vector database](https://www.meilisearch.com/blog/what-is-a-vector-database). Some common vector databases are Pinecone, Milvus, FAISS, and Weaviate.

## Why is indexing important in RAG?

RAG indexing makes RAG systems more accurate by organizing information so the LLM can quickly find and use the right content when answering a question. 

Here are some of its benefits:

* **Speed:** Without indexing, the system would have to scan every document in its entirety whenever someone asks a question. Indexing stores precomputed embeddings, so the system can instantly search and retrieve relevant chunks without having to search from scratch.
* **Relevance:** Because chunks are embedded and tagged with metadata, the algorithm can match a user’s query to the most meaningful pieces of text. This precise retrieval enhances the relevance of answers.
* **Accuracy:** Indexing preserves context and meaning. Since the retrieval step is cleaner and more targeted, the final answer generated is better grounded in correct information.
* **Scalability:** Vector databases and indexing techniques can elegantly handle large data volumes. Therefore, it is possible to update or expand the knowledge base over time without affecting the RAG system's performance. 

## What are the main RAG indexing strategies?

Different indexing strategies fit different [types of RAG](https://www.meilisearch.com/blog/rag-types). Picking the right one mainly depends on the content you want to search through. 

Your indexing strategy can also impact the relevance and performance of the RAG. 

Here are several common RAG indexing strategies and when you would use them:

### 1. Hierarchical indexing

Hierarchical indexing involves organizing content at multiple levels. The text resource is broken down from a document into sections, then paragraphs, and finally chunks. This strategy respects a document's structure and does not randomly create chunks. 

For example, a legal contract might first be identified by section (e.g., ‘Termination Clause’) then by the exact paragraph that answers the user query. 

You can use this technique when your documents, such as manuals or textbooks, are long and logically organized. It reduces noise, improves relevance, and allows the retriever to first narrow down the section before searching in-depth.


### 2. Hybrid indexing

Hybrid indexing combines keyword-based search (e.g., BM25) with vector search, similar to [hybrid search](https://www.meilisearch.com/blog/hybrid-search). The AI system uses both approaches to improve its search accuracy. 

For example, when searching for ‘how to fix my damaged electric kettle,’ a keyword search identifies documents containing those exact terms. A vector search retrieves related content to help fix damaged electrical appliances. 

This strategy is ideal when you want precision and coverage. It is also useful when migrating from classic search tools to [RAG tools](https://www.meilisearch.com/blog/rag-tools) without losing keyword search performance.

### 3. Time-based Indexing

Time-based indexing is used to keep track of information that changes over time. Each document or section is stored with a date label, so the AI system knows which are the most recent. 

This strategy is useful for materials that are frequently updated, such as financial reports, company guidelines, or news articles. 

### 4. Multi-representation indexing

Multi-representation indexing stores multiple vector embeddings for the same piece of text within a single retrieval system. Each embedding focuses on a different aspect of the content. One might capture the overall idea, another might emphasize technical language. 

When a user types a search query, it is processed across all versions. 

This method works well in areas where information can be interpreted in several ways. It is also useful when precision is essential and the language used in your documents is specialized.

### 5. Domain indexing

Domain indexing is designed to match the terminology of a particular field. For example, in medical data, the system might use drug names or symptom categories to label chunks. In software documentation, the index could be organized by API endpoints or code use cases. 

This approach works best when your material follows clear patterns and uses consistent terms. 

It improves search relevance because the index is built around how professionals in that field naturally search for information, rather than treating all text the same.


### 6. Graph-enhanced indexing

Graph-enhanced indexing is a strategy that links text chunks (called nodes) using relationships (called edges). Nodes could include topics, categories, citations, and related details. Graphs allow you to quickly see how multiple chunks are connected. 

Use this strategy in situations when relationships are more important than similarity. It is also ideal for complex domains, such as legal matters or academic research, that involve interconnected concepts.

## What are common challenges in RAG indexing?

RAG indexing involves choices that affect speed, accuracy, and long-term maintenance. 

Here are some common challenges and how you can overcome them:

* **Handling large datasets:** When you are working with thousands of documents (or millions of chunks) indexing and searching can be incredibly slow. Storage costs also go up. A good solution would be to use sharding, compress embeddings, or remove low-value content rather than indexing everything.
* **Picking the right chunk size:** Chunks that are too long can provide the wrong context, and chunks that are too short can lose their meaning. To find the right balance, test with different token lengths (e.g., 200, 400, 600) and evaluate their retrieval quality before deciding on a chunk size.
* **Keeping embeddings consistent:** If you change the embedding model, older vectors may no longer match the new ones. That can lead to bad retrieval. To avoid this, version your embeddings and re-embed only affected documents when the model changes.
* **Stress in managing metadata:** Messy or inconsistent metadata makes filtering useless. You can fix this by defining an explicit schema (e.g., source, date, format, or access level) from the start and enforcing it during ingestion.

## What are the best practices for RAG indexing pipelines?

The quality of your chunks, embeddings, and metadata directly shapes what the AI model can find and use. Here are some best practices for building and maintaining RAG indexing workflows:

* **Always start with clean and meaningful data:** Never dump everything into the index. Remove duplicates and filter out low-value text, such as headers, navigation menus, or disclaimers. Cleaner input means cleaner retrieval and fewer irrelevant matches later.
* **Chunk by meaning, not by fixed length:** Instead of automatically slicing text every 300 tokens, break it around natural boundaries. This includes headings, paragraphs, bullet lists, or API endpoints. It keeps context intact and reduces confusion during retrieval.
* **Keep embeddings and models aligned:** If you update your embedding model or retriever, do not leave the index behind. Re-embed over time or version your vectors so that the old and new chunks stay compatible.
* **Always add metadata:** Good tags make filtering more powerful. Try adding tags such as source, document type, date, section, or access level. This helps the system narrow results quickly and stay grounded in the proper context.
* **Use smart retrieval tricks:** Hybrid search (combining keywords and vectors), light reranking, and query rewriting for typos boost recall without flooding the results with junk.
* **Monitor and refresh the index:** Always track retrieval quality, stale documents, and storage bloat. In addition, replace outdated content and reduce noise instead of letting the index grow unchecked.

### How Meilisearch helps

Meilisearch supports vector search and hybrid querying, which makes it a good fit for RAG indexing pipelines. You can store your text chunks and their embeddings directly in the index, instead of using a separate vector database. That means your indexing step can include both chunking and attaching embeddings.

Meilisearch also lets you filter by metadata such as source, tags, or date, which keeps retrieval grounded and avoids random matches. 

Since Meilisearch handles both semantic search and keyword search, you can combine exact terms with vector similarity. This is perfect when your users mix natural phrasing with domain-specific words.

Another bonus is speed! Indexing is fast, and updating entries doesn’t require a complete rebuild. That helps when documents change or you need to refresh embeddings. In a RAG setup, Meilisearch acts as a searchable store for meaning and structure without overcomplicating the pipeline.

## When should you update a RAG index?

You should update the RAG index whenever the data, documents, or the embedding model change in a way that could affect retrieval quality.

Suppose you add new documents, update policies, change pricing, publish fresh blog posts, or remove outdated material. The index needs to be refreshed so the AI system does not return inaccurate answers. 

The same applies if the embedding model gets upgraded. Old vectors will not match new ones, so re-embedding is needed. 

In fast-changing domains, updates should be scheduled daily or weekly. Monthly or incremental updates may be enough for slower-moving content. 

A good rule of thumb is that if a human wants the new information reflected in the answers, the index should be updated.

## How can you evaluate RAG indexing performance?

You can assess RAG indexing by measuring how fast the system retrieves results, how relevant they are, and whether users find the answers helpful. 

* **Speed:** Slow searches usually indicate a poorly structured index. 
* **Semantic accuracy:** A good question to ask here is, “Are the top chunks related to the query, or just loosely similar?” You can answer this with small human-reviewed samples or automated relevance checks. If you are working with structured data (such as product tables or policy fields) and unstructured data (such as PDFs or help articles), make sure your index handles both consistently.
* **User satisfaction:** Track follow-up questions, unanswered queries, and user feedback to identify areas for improvement. For more structured testing, you can use evaluation tools or custom scripts that measure how often your system retrieves the correct results. If retrieval feels slow or unreliable, your indexing pipeline probably needs fine-tuning, better chunking strategies, or fresher embeddings.

## What are the most popular vector stores for RAG indexing?

Some of the most popular vector stores (or vector-backed retrievers) used in RAG pipelines are Meilisearch (with its vector support), Pinecone, Weaviate, Chroma, Milvus, Qdrant, and FAISS (or pgvector).

* **Meilisearch:** Though not a traditional vector database, you can use it as a vector-backed retriever by storing embeddings alongside documents. It is excellent when you want a unified search layer (both keyword and vector) without managing a separate vector store. Choose Meilisearch if your dataset is moderate and you value simplicity and lightweight operations.
* **Pinecone:** Excellent if you do not want to deal with servers or scaling issues. It is fully managed, fast, and built for production so that you can focus on retrieval, not infrastructure.
* **Weaviate:** Open source and flexible. It handles hybrid search, filtering, schemas, and even relationships between data. It’s a good fit when you care about meaning and structure, not just raw embeddings.
* **Chroma:** Simple and developer-friendly (built with Python users in mind). It is often used in quick RAG prototypes or smaller RAG applications. If you are experimenting or do not have many vectors, Chroma is a good vector store to use.
* **Milvus:** Milvus is designed for scale. It is open source and works well for storing and querying millions of embeddings across machines. You should use it when performance and control matter more than convenience.

### How to choose a vector DB for RAG indexing

When building a RAG system, your vector database is the backbone of the retrieval system. It needs to store embeddings efficiently and return relevant results quickly. The goal is not just picking a popular tool, but choosing one that fits how you plan to use it:

* **Speed:** If your app needs quick responses, latency matters a lot. A good vector database should return similar results fast, even when working with many embeddings.
* **Scalability:** Your data might start small, but it will not stay that way. The database you choose should handle growth without turning into a maintenance nightmare. If you expect to move from thousands to millions of vectors, choose a tool that can scale horizontally without losing performance.
* **Integration:** You do not want to spend hours wiring things together. Choose a database that works efficiently with your infrastructure. Clean APIs and SDK support make the development process smoother.
* **Cost:** Some options are fully managed and save you time, but they can get pricey as usage grows. Open-source tools are cheaper to run but require more hands-on setup and maintenance. It is all about balancing convenience with budget.

Let’s see how RAG indexing differs from traditional search indexing.

## How does RAG indexing differ from traditional search indexing?

RAG indexing and traditional search indexing seem similar on the surface, but they work in very different ways. 

Traditional indexing is all about keywords. 

RAG indexing, on the other hand, focuses on semantic meaning. Instead of relying solely on words, it uses embeddings to capture context and retrieve relevant chunks based on semantics.

![RAG Indexing vs. Traditional Search Indexing.png](https://unable-actionable-car.media.strapiapp.com/RAG_Indexing_vs_Traditional_Search_Indexing_6b6b7f9ad7.png)

RAG indexing shines when users do not use the exact wording found in the source documents. Examples are conversational queries, domain jargon, or multilingual content. 

Traditional indexing is still a good option when speed, exact matches, and lightweight infrastructure are priorities. Examples are documentation sites or e-commerce search results.

## What are future trends in RAG indexing?

RAG indexing is evolving rapidly, and the next wave involves combining smarter context handling with more effective retrieval strategies:

* **Graph-powered retrieval:** Instead of treating documents as isolated chunks, newer trends link concepts using knowledge graphs. This approach captures relationships across topics and returns more connected answers.
* **RAPTOR-style hierarchical chunking:** Approaches inspired by RAPTOR break content into layers (small chunks are broken for precision, and larger summaries are broken for broader context). This ensures that information retrieval can adapt to the query’s depth.
* **Hybrid retrieval pipelines:** Mixing keyword search, semantic vectors, and metadata filters is becoming the norm. This combination boosts accuracy and speed without relying on a single retrieval process.

## Index smarter, answer sharper

Indexing is a critical component of a good RAG system. When the index is well-chunked and backed by good embeddings, your model will efficiently retrieve the correct information.

However, a smart index is not something you set and forget. As new content springs up, the index should evolve too. Even minor improvements (such as better metadata, refreshed embeddings, or reorganized chunks) can noticeably improve the retrieval accuracy. 

At the end of the day, sharper answers do not come from luck or bigger models. They come from indexing that is done with intent.  

RAG indexing: Structure and evaluate for grounded LLM answers

Semantic search and retrieval-augmented generation (RAG) are crucial for how today's AI systems handle information and find relevant answers to user queries. 

Think of it this way: semantic search is all about ranking information based on what it means, while RAG takes it further by actively retrieving knowledge to provide the answers users need. 

In this article, we're going to explore:

* What semantic search and RAG are and how they work.
* The contrast between their capabilities.
* The potential for overlap and collaboration, as well as whether or not they can work without each other.
* How to figure out when semantic search is enough, when RAG is essential, and when combining them gives you the best results.
* The pros, cons, and limitations that come with both so that you can evaluate them properly.

By the time you're done, you'll have a clear framework for choosing the best approach for your data and users. 

Ready? Let’s get into it.

## What is semantic search?

[Semantic search](https://www.meilisearch.com/blog/semantic-search) is a technique that helps AI systems understand the intent behind user queries instead of relying on exact keyword matching. It uses embeddings, vector representations, and natural language processing (NLP) to map words by meaning. 

By doing this, semantic search retrieves the most relevant documents even if the query phrasing isn’t perfect. Unlike traditional information retrieval methods, semantic search ranks results based on context and relationships, and produces answers that genuinely make sense. 

Teams can also explore [choosing the best model for semantic search](https://www.meilisearch.com/blog/choosing-the-best-model-for-semantic-search) to refine their search architecture.

## What is RAG (retrieval-augmented generation)?

[Retrieval-augmented generation](https://www.meilisearch.com/blog/what-is-rag) (RAG) combines information retrieval and generative AI to create more factual and contextually relevant responses. It retrieves relevant documents from an external knowledge base or vector database using embeddings before passing them over to the large language model (LLM). 

From there, the LLM uses the retrieved context and ensures that the response accuracy is high and the hallucinations are nonexistent. 

In short, RAG augments the model’s knowledge with real data, making responses more trustworthy and domain-specific.

## How does semantic search work?

[Semantic search](https://www.meilisearch.com/blog/semantic-search) goes beyond keyword matching by understanding the meaning and intent behind those keywords through contextual relationships.

![How Semantic Search Works.png](https://unable-actionable-car.media.strapiapp.com/How_Semantic_Search_Works_77696c3be8.png)

* **Step 1:** Semantic search converts text into embeddings. Each query and document is transformed into dense [vector representations](https://www.meilisearch.com/blog/what-are-vector-embeddings) that capture semantic meaning.
* **Step 2:** Then, it compares embeddings. The system measures cosine similarity or Euclidean distance between the user query and stored vectors to find the most relevant results.
* **Step 3:** Finally, a retriever ranks documents not just by word overlap but also by conceptual closeness, bringing up the absolute best matches.

This process helps users find relevant information even when they aren’t sure of the exact wording needed to get what they’re looking for.

## How does RAG work?

RAG connects advanced retrieval systems with LLMs to produce answers that the users are looking for. 

![A graphic showcasing how RAG works.](https://unable-actionable-car.media.strapiapp.com/How_RAG_Works_b6a617b59f.png)

Let’s see how it works:

* **Step 1:** First, the data is embedded and indexed. Documents from various data sources are split into chunks, converted to vector embeddings, and stored in a vector database.
* **Step 2:** When a user query is submitted, the LLM first evaluates whether it can answer the query on its own. If it needs additional context to provide a satisfactory answer, this is where RAG comes into play.
* **Step 3:** The RAG retriever doesn’t search through the internal vector database. It can access external knowledge bases or even search the internet to find relevant documents.
* **Step 4:** Once RAG provides more context, the LLM uses both the query and the retrieved context to generate more relevant answers. 

As a result of this whole cycle, the answers you get are not only relevant but up-to-date as well.

## What is the difference between semantic search and RAG?

The main difference between semantic search and RAG is what they return. Semantic search only finds specific information, while RAG provides an explanation, enhancing the response to the user query. 

Semantic search works by matching user queries to document vector embeddings using similarity metrics such as cosine or Euclidean distance. It's super-efficient, fast, and great for providing direct answers closely related to the user query.

RAG, on the other hand, feeds retrieved context to a large language model to compose a more elaborate answer. This added step aims to reduce AI hallucinations and improve accuracy.

The table below highlights other key distinctions between the two:

![A table highlighting the key differences between semantic search and RAG.](https://unable-actionable-car.media.strapiapp.com/Semantic_search_vs_RAG_828bd84d6d.png)

You can also explore different [RAG types](https://www.meilisearch.com/blog/rag-types) to understand how architectures vary in retrieval and generation workflows.

## What problems does semantic search solve?

Keyword-based retrieval has always had limitations. Semantic search addresses this limitation by covering a broader domain than just literal keywords. 

Here’s what it solves:

* **Intent detection:** It interprets what the user means, not just what they type. 
* **Disambiguation:** Semantic search removes ambiguity and distinguishes between words with multiple meanings by analyzing their embeddings.
* **Better recall and precision:** It retrieves relevant documents even when queries use different phrasing. 
* **Multilingual and domain-specific results:** Diversity plays a huge role in getting the correct answer to the user. Semantic search adapts across knowledge bases and domain-specific datasets without manual rules.
* **Improved user experience:** Users love semantic search because it ranks relevant information intuitively, helping them find what they need faster.

By combining natural language processing (NLP) and vector embeddings, semantic search ensures users get the results they need.

## What problems does RAG solve?

RAG addresses the main challenges of retrieval and generation by grounding large language models in real data:

* **Hallucinations:** RAG retrieves relevant documents before generation, reducing the risk of made-up or unsupported answers.
* **Outdated knowledge:** It connects to external knowledge bases to keep responses accurate and up to date.
* **Context limitation:** It provides retrieved context beyond the LLM’s memory, thereby improving factual grounding.
* **Complex reasoning:** It enables the model to combine multiple information sources to produce multi-step answers.
* **Trust and explainability:** It cites its sources, ensuring responses are transparent and verifiable.

By combining semantic retrieval with generative models, RAG helps users get precise, context-aware answers rather than vague summaries, using techniques described in our [RAG techniques](https://www.meilisearch.com/blog/rag-techniques) article.

## Can semantic search be used without RAG?

Yes, semantic search can work perfectly well without RAG in many real-world applications. It’s often used when retrieving relevant information is sufficient and generating new text isn’t necessary.

For instance, knowledge bases, search engines, and customer support chatbots rely on semantic search to quickly find the most relevant documents. It’s also used in question-answering systems, e-commerce search, and internal document retrieval, where users want direct access to existing content rather than a generated summary.

Because it operates only on vector embeddings and retrieval algorithms, it’s lightweight, scalable, and cost-efficient.

However, when users expect natural language answers or context synthesis across multiple documents, semantic search alone falls short. That’s when RAG becomes the logical next step.

## Can RAG work without semantic search?

Yes, it can, but it’s not ideal. RAG can technically operate using keyword retrieval or metadata filtering instead of semantic search, but the results are less precise. 

Without vector embeddings or semantic similarity, the retriever may fetch documents that match words but miss intent. And intent is the key in any retrieval.

Some teams use BM25 or hybrid search engines as the retrieval layer for smaller or domain-specific datasets where context is already well structured. This approach simplifies infrastructure and lowers cost, but limits recall accuracy and context coverage.

Skipping semantic search means the model depends more on the generator to interpret data. This is a risky tradeoff that can lead to hallucinations or shallow grounding. 

For most real-world RAG systems, semantic search is the foundation that makes retrieval meaningful before generation is even a thought.

## What are the pros of semantic search?

You already know the accuracy aspect of semantic search. Let’s look at some more pros:

1. **Understands user intent way better:** Semantic search interprets user queries based on meaning, so the retrieved results are better than in plain keyword matching.
2. **Deals with ambiguity like a pro:** Through NLP, it can distinguish between different meanings, such as ‘Apple,’ the company, versus ‘apple,’ the fruit.
3. **Enables scalability:** Semantic searchcan retrieve results quickly and accurately, even across large datasets.

Semantic search is the foundation for both modern search technologies and RAG systems.

## What are the pros of RAG?

RAG provides language models with a way to remain factual, grounded, and context-aware without retraining every time new data arrives. 

Let’s look at some pros of RAG: 

1. **Has access to real-time knowledge:** It retrieves relevant documents from external knowledge sources.
2. **Goodbye hallucinations:** By relying on answers from the retrieved context, RAG minimizes the introduction of made-up or outdated information.
3. **Adapts to various domains easily:** It easily plugs into domain-specific datasets, from medical records to legal documents, without the need to fine-tune the LLM.

Overall, RAG bridges semantic search and generative AI to help teams create applications that not only find data but explain it in natural language.

## What are the limitations of semantic search?

Let’s look at some semantic search limitations. 

1. **Complex queries might result in ambiguity:** Useful as it is, semantic search can falter when queries are too extensive.
2. **Costly if you want to handle massive datasets:** Creating and updating vector embeddings for large datasets demands increasing amounts of resources, which can be expensive.
3. **Potential for low-quality results:** If the stored data is sparse or of poor quality, you might get low-quality results.
4. **Generative reasoning is lacking:** It can’t produce detailed answers, which makes it less helpful in question-answering or chatbot use cases.

These limitations make semantic search excellent for discovery, but not enough when users expect complete, natural-language answers.

## What are the limitations of RAG?

RAG is renowned for its accuracy, but the cost of this accuracy is the added layers of complexity. 

Let’s explore some other limitations of RAG:

1. **Higher latency and cost:** Multiple API calls that enable RAG to be so effective come with hefty charges.
2. **Variable retrieval quality:** If relevant documents are missing or poorly embedded, the LLM will still hallucinate.
3. **Complex infrastructure:** With increasing overhead from vector databases, chunking, and retrieved context pipelines, the RAG system can become really complex.
4. **Difficult to evaluate:** You need continuous assessment of the RAG system to maintain retrieval quality and accuracy.

If you’re working in high-stakes domains that involve knowledge-based systems, RAG is perfect. But it might be a bit too much for simpler applications.

## How is semantic search evaluated for effectiveness?

Semantic search effectiveness is measured by how accurately it retrieves and ranks relevant documents for a given user query. After all, that’s what it is for.

The primary metrics include precision (how many retrieved results are relevant) and recall (how many relevant results were retrieved). 

![A graphic explaining precision and recall, two primary search evaluation metrics.](https://unable-actionable-car.media.strapiapp.com/Precision_and_recall_9d1f66c31e.png)

Other factors, such as Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (nDCG) help assess ranking quality.

Teams also use user satisfaction surveys or click-through rates (CTRs) to measure success. These evaluations often compare how well embeddings, retrievers, and algorithms capture intent across datasets.

In short, good semantic search is about balancing accuracy, relevance, and scalability.

## How is RAG evaluated for effectiveness?

Evaluating a RAG system requires measuring not only retrieval accuracy but also how well generated answers remain grounded in the retrieved context.

The key metrics for this include factual accuracy, grounding score, and hallucination rate (the frequency with which the LLM adds unsupported information). Teams also track context precision and coverage to assess how the retrieved chunks influence the model’s response.

Other evaluation methods test retrieval quality, latency, and user trust over time. These insights help teams balance precision and performance across different data sources.

For a deeper look into evaluation frameworks, read our guide on [RAG evaluation](https://www.meilisearch.com/blog/rag-evaluation), which breaks down real-world techniques for measuring grounded generation.

## What are the latest trends in semantic search and RAG?

There’s an upward trend in the popularity of both semantic search systems and RAG. Especially now, when AI and LLMs are a core part of almost every enterprise application. 

The two major trends you need to keep up with are:

1. **Integration between LLMs and retrieval systems:** Modern retrieval systems have developed the capability to integrate LLMs and deliver answers that are not only more accurate but also sound conversational and almost human.
2. **Hybrid approach to retrieval:** By combining keywords, vector embeddings, and reranking techniques, we can achieve significant improvements in precision and recall across both structured and unstructured data.

This set of trends, along with some others, contributes to improving RAG daily. In essence, they are all about making AI search engines more intelligent and user-friendly.

## Frequently Asked Questions (FAQs)

The following FAQs should address any remaining confusion about semantic search and RAG.


### Is RAG the same as semantic search?

No. Semantic search ranks documents based on their meaning. RAG retrieves the documents first, and then uses this retrieved context to generate answers the users are looking for. 


### Why is semantic search important for AI?

Semantic search improves how AI understands user queries. It makes the AI interpret the intent behind the search instead of just taking the keywords at face value. Naturally, this brings a significant boost in accuracy and the user experience.


### Why is RAG used in generative AI?

Generative AI needs the right context to generate information. RAG helps LLMs combine retrieved information with the user query to generate the required answers. This significantly reduces AI hallucinations and lays the foundation for real-time AI agents or chatbots.


### What is the role of embeddings in semantic search?

Embeddings are the backbone of semantic search because they convert text into vector representations that capture meaning. They enable the retriever to find semantically related content even when users don’t use the exact keywords in their search. You can think of them as interconnected neural networks.


### What is the role of embeddings in RAG?

In RAG systems, embeddings serve as connectors. They connect the retriever and the generator. They help retrieve the most relevant documents that match a query’s meaning before passing them to the LLM to generate an informed answer.


### How does semantic search compare to vector search?

Semantic search uses advanced algorithms to understand the meaning of words and sentences. It's like a map where similar ideas are close together. This helps it find answers that make sense, even if the exact words aren't there, because it understands what you're trying to find, as detailed in this [semantic vs. vector search](https://www.meilisearch.com/blog/semantic-vs-vector-search) article.

## Harnessing the power of precision

The main goal of most advancements today is to guide AI towards greater precision.

RAG and semantic search are both unique in their own ways, with distinct sets of pros and limitations. However, this does not stop them from being a force to be reckoned with when used together in a hybrid setting.

Semantic search vs. RAG: A side-by-side comparison

When working with large language models, achieving accurate, relevant results depends on several factors. 

Two of them are Retrieval-Augmented Generation (RAG) and prompt engineering. 

RAG helps generate better answers by allowing the generative AI model to retrieve information from external knowledge sources, keeping responses fact-based and up to date. 

Prompt engineering, on the other hand, focuses on how you communicate with the AI to produce clear and context-aware answers. 

* RAG adds new knowledge without retraining, whereas prompt engineering improves the model’s use of its existing knowledge.
* RAG works well for research, legal, healthcare, and customer support; prompt engineering is well-suited for summarization, creative writing, Q&A, and data formatting.
* RAG is complex and can be slower, while prompt engineering is cheaper but requires skill.
* RAG requires more setup and maintenance, but prompt engineering is faster and cost-effective.

Choosing the right approach depends on what you want: accuracy, freshness, or speed. Often, combining both gives the best results. Let’s dig deeper into this. 

## What is prompt engineering?

Prompt engineering is the art of crafting clear instructions for large language models (LLMs) to achieve the best possible results. 

Think of it like learning to ask the right questions. A vague prompt might produce a generic answer, while a carefully worded one can give you precisely what you need. 

Prompt engineering is about understanding how the gen AI model interprets instructions and using that insight to guide it toward your desired output. 

## What is RAG (retrieval-augmented generation)?

[RAG](https://www.meilisearch.com/blog/what-is-rag) is a technique that improves LLM outputs by providing them with access to external resources. This could include databases, documents, and APIs. Instead of relying solely on what they were trained on, RAG allows AI models to access additional information when generating answers. 

For instance, we can give an AI model both memory and internet search skills, so that it knows today’s date and searches the internet for the most recent data.

## How does prompt engineering work?

Prompt engineering is about learning how to interact with an artificial intelligence model in a way that generates the best possible response.

As a rule of thumb, it is good practice to provide the LLM with enough context to answer your questions. LLMs are trained to follow user instructions, and they do so within the context they have. 

For example, if you ask an LLM to act like a hip-hop artist, its response will sound like one. Ask it to write like a Nobel Prize–winning author, and it will adapt its responses accordingly.

Finding the right prompt involves experimentation. If the answer from an initial prompt is not accurate, you can refine your prompt to provide more context and make it clearer. You may also check for noise or contradictory statements and eliminate them. 

For example, if you do not have a background in science, instead of asking the model to “Explain photosynthesis,” you could say, “You have over a decade of experience teaching biology. Explain photosynthesis like you are teaching someone in eleventh grade.” The goal is to give the AI enough context and direction to produce relevant responses.

## How does RAG work?

RAG works by combining retrieval and generation so that an LLM can answer using external facts. 

In the retrieval phase, RAG searches external sources to find the most relevant information, and in the generation phase, the model uses that retrieved data to craft an accurate response. 

![How RAG Works.png](https://unable-actionable-car.media.strapiapp.com/How_RAG_Works_e26007ec0c.png)

Here is how the process goes, step-by-step:

1. A user types in a search query. 
2. A retriever searches a document store or database for information that matches the query. 
3. The information is ranked, and the best is selected to form a context. 
4. The original question and the context are sent to the LLM. 
5. The model generates a response grounded in the retrieved information. 
6. Finally, a verifier or re-ranker checks the model's outputs before they are returned. This is optional.

## What are the main differences between RAG and prompt engineering?

The main difference between RAG and prompt engineering is how they help AI models produce better answers. 

Different [RAG types](https://www.meilisearch.com/blog/rag-types) improve accuracy by allowing the model to retrieve real-time data before generating a response. 

Prompt engineering focuses on phrasing and optimizing questions to get the best result from the model’s existing knowledge. 

To sum it up, RAG gets its information externally, while prompt engineering uses information already within the AI model.

![RAG vs. Prompt Engineering.png](https://unable-actionable-car.media.strapiapp.com/RAG_vs_Prompt_Engineering_a00885536d.png)

Now, let’s discuss the benefits and challenges of using RAG and prompt engineering.

## What are the advantages of prompt engineering?

Prompt engineering can help users get the most out of AI tools without requiring additional data. 

Here are some of the advantages:

* **Better control over responses:** You can guide the AI model to produce more relevant outputs by learning how to phrase questions effectively. For example, a marketer can ask ChatGPT to ‘act like an expert marketer in a Fortune 500 company and write an email to promote a new product,’ and receive a good draft email immediately.
* **Works across different tasks:** Prompt engineering can be applied across various sectors. It is helpful for specific tasks such as coding, summarizing reports, writing essays, and even brainstorming ideas. 
* **Saves time and effort:** Making your input prompts clearer reduces the need for multiple iterations. Instead of constantly reworking the answer, a well-crafted prompt helps the AI deliver the desired result in a single go.
* **Encourages creativity and personalization:** Prompt engineering gives users the freedom to set their own tone or style. This makes it an excellent tool for writers, designers, or educators who desire unique results.


## What are the advantages of RAG?

RAG has several benefits that make AI responses more helpful in real-life situations.

* **Improves accuracy:** RAG reduces the risk of errors by pulling information only from verified external sources.
* **Access to up-to-date information:** RAG can retrieve current information from live databases or documents. This makes it perfect for fast-changing topics, such as the news.
* **Better context understanding:** RAG provides the AI model with additional background information before generating an answer, helping it better understand the question and respond more accurately.
* **Flexibility across domains:** Because it can connect to various data sources, RAG can easily adapt to resource-intensive, specialized fields like healthcare, agriculture, or finance while still delivering insights tailored to each domain.
* **Reduced AI model training costs:** Since RAG relies on retrieval rather than constant retraining, organizations can update knowledge from data sources rather than rebuilding the entire model for each process.

## What are common challenges with prompt engineering?

Prompt engineering is a powerful skill, but it is not always straightforward. Generating the perfect response from AI often takes creativity and practice. 

Here are some common challenges:

* **Inconsistency in results:** Just a slight change in wording can completely alter the AI’s answer. Also, because AI is non-deterministic, what works once may not work the same way next time, making it hard to achieve consistent responses.
* **A need for expertise:** Crafting a strong prompt requires some level of domain knowledge and an understanding of how the AI model interprets instructions. Newbies may give generic prompts, leading to generic AI responses.
* **Can be time-consuming:** Refining your prompts through trial and error can take a long time, especially when dealing with complex tasks. If not properly managed, this can slow down your workflow or even reduce your productivity.
* **Model limitations:** AI systems only know the training data on which they were built. Therefore, even the best prompt cannot fix a model whose knowledge base is lacking. Such situations lead to hallucinations or outdated responses.

## What are the common challenges with RAG?

Some challenges can make RAG implementation difficult, even though it greatly improves accuracy and relevance. 

Here are some of them:

* **System complexity:** RAG can be complex to set up and maintain, especially for non-technical teams. It requires [connecting a retriever and a database to the LLM](https://www.meilisearch.com/blog/how-do-you-search-in-a-database-with-llm).
* **Integration overhead:** Integrating a RAG system into an existing application or workflow often requires extra resources. You have to set up the RAG infrastructure while maintaining the external data sources at the same time.
* **Latency and speed issues:** Balancing accuracy with performance can be challenging with RAG. Since it searches and retrieves data before generating a response, it can be slower than the traditional models, especially when handling large datasets. 
* **Information retrieval error:** Sometimes a RAG system retrieves the wrong documents, leading to answers that sound confident but are based on inaccurate data. This makes proper data indexing and filtering critical when setting up the RAG system.

## What are examples of prompt engineering use cases?

Prompt engineering can be applied in many real-world situations to make AI tools more valuable and efficient: 

* **Question answering:** Businesses use prompt engineering to make chatbots and help desks smarter. With the right prompt, an AI agent can provide clear and accurate responses to customer or employee questions.
* **Text summarization:** It is excellent for turning long text into short but meaningful takeaways. For instance, you can say, ‘Summarize this report in three simple points,’ and save hours of reading time.
* **Data conversion:** Prompt engineering can help you quickly clean or restructure information. For example, changing your raw notes into a professional report.
* **Creative writing:** Writers use prompts to get innovative ideas, explore storylines, or overcome creative block.

## What are examples of RAG applications?

[RAG tools](https://www.meilisearch.com/blog/rag-tools) are powering many of the apps we use today. Here are a few ways RAG is being used in the real world:

* **Customer support systems:** Many customer service chatbots now use RAG to fetch real answers from company manuals. So when you ask your bank’s chatbot about your account details, it gives you the exact information instead of a generic reply.
* **Legal research:** Law firms use RAG to review thousands of case files and contracts in seconds. A lawyer can ask about precedents for a case and instantly get relevant examples instead of manually searching the archives.
* **Healthcare and medical support:** Doctors and researchers use RAG to find the latest studies, treatment guidelines, and patient data. This enables them to make more informed medical decisions.
* **E-commerce product comparison:** RAG is used by e-commerce companies to compare products. It retrieves product specs, reviews, or prices from the database and generates personalized recommendations.

## Can RAG and prompt engineering be combined?

Yes, RAG and prompt engineering work better together. 

For example, when someone asks, ‘Summarize this customer’s issue in three bullet points,’ a RAG setup might pull a customer’s record from the database, and a good prompt allows the LLM to summarize only the issues returned. 

In legal work, RAG can find relevant cases, which are then combined with a prompt that says, ‘Compare these rulings and highlight the key differences.’ This provides a concise, actionable comparison rather than a long list that might be difficult to interpret. 

In healthcare, RAG retrieves patient history and recent medical studies, while prompts ensure the results follow clinical reporting standards.

RAG handles the ‘what’ (or the facts), and prompt engineering handles the ‘how’ (or the presentation). When used together, they make answers both relevant and accurate.

## What are the costs of RAG vs. prompt engineering?

The costs of setting up RAG and prompt engineering are different. 

RAG can be expensive to implement because it requires additional infrastructure (such as a database, a retrieval system, and integration tools). There is also a maintenance cost for updating data sources, handling storage, and ensuring the retrieval system stays accurate and fast. 

Prompt engineering, on the other hand, is far cheaper because it mainly involves using your time and skill. You have to learn how to design effective prompts and also test them for consistent results. There is no need for new servers, APIs, or databases, making it suitable for quick, low-cost projects.

RAG requires more upfront investment but delivers higher accuracy, while prompt engineering is cost-effective and easily scalable for smaller applications.

Want to decide whether to use RAG or prompt engineering? Let’s see the factors you should consider when choosing.

## How do you choose between RAG and prompt engineering?

Choosing between RAG and prompt engineering depends on what you need from your system and the kind of resources you have. 

Here is a simple decision guide to use:

* **Check if you need fresh data:** RAG is the way to go if your answers must include the latest information. But if you are working with general knowledge that doesn't change often, prompt engineering should be sufficient.
* **Weigh your accuracy and reliability needs:** For fields like healthcare, law, or finance, where mistakes can be costly, RAG is safer because it cites real sources. Prompts alone cannot always guarantee accuracy. 
* **Consider your time, budget, and skills:** RAG takes more setup and maintenance, while prompt engineering requires creative thinking and practice. If you have limited resources, then go with prompt engineering.
* **Balancing performance and workflow:** RAG systems can be slightly slower since they search external data before responding, but the answers are usually more accurate. Prompt engineering is quicker and is perfect for lightweight applications where speed matters more than depth.

## How does RAG compare to fine-tuning?

The main difference between [RAG and fine-tuning](https://www.meilisearch.com/blog/fine-tuning-vs-rag) is that RAG pulls in fresh information from external sources when needed, while fine-tuning actually trains the model to learn that knowledge internally.

With fine-tuning, you take a pre-trained model and feed it domain-specific data (such as legal documents or customer records) so that it learns the patterns and language of that field. This makes it faster and consistent. 

The limitations are that it is expensive to train, requires lots of clean data, and must be redone whenever new information comes in.

RAG, on the other hand, does not retrain the model. Instead, it connects to a database or document store and retrieves the most relevant information whenever a question is asked. This ensures the answers are up to date and adaptable to new topics.

## How does prompt engineering compare to fine-tuning?

The main difference between prompt engineering and fine-tuning is that prompt engineering focuses on how you ask the model your question to get the best response, while fine-tuning changes what* *the model actually knows.

With prompt engineering, you are not retraining anything. You are instead crafting the proper instructions to guide the model’s behavior. It is fast, affordable, and flexible, making it perfect for tailoring outputs to specific needs. 

The catch is that results can vary greatly depending on how well the prompts were written.

Fine-tuning, on the other hand, takes things deeper. You train the model on new data so it permanently learns that knowledge. This gives you consistency and expertise but comes with higher costs, more setup time, and the need to continuously retrain to stay up to date.

## RAG vs. prompt engineering: and the winner is...

The reality is that both techniques shine in their own way. 

RAG is your go-to tool when accuracy and up-to-date information matter most, since it can retrieve from live data sources. Prompt engineering is the fastest, most flexible way to generate outputs without retraining any model.

If you are building something that needs freshness, go with RAG. For quick, creative control, prompt engineering takes the lead. 

RAG vs. prompt engineering: choosing the right approach for you

GraphRAG vs. Vector RAG: Side-by-side comparison guide

We're kicking off [Launch Week](https://www.meilisearch.com/launch-week-q3-2025) with something we've been excited to share: Meilisearch Chat, a new way to make your data conversational.

For the first time, you can turn your existing Meilisearch index into an AI-powered chat experience – without complex infrastructure, separate vector databases, or months of custom development.

## Search is becoming a conversation

Your users and buyers don't want to search anymore – they want to ask. They expect apps, documentation, and internal tools to understand natural language and give them direct answers, not endless lists of results.

Until now, that's been really hard to deliver. Traditional conversational AI required juggling multiple LLM calls, vector databases, reranking pipelines, and endless tuning. The complexity alone made it expensive and slow, often taking months just to get a working prototype.

Meilisearch Chat changes all of that.

## One API call, complete conversational AI

The new `/chat` brings AI-native search to your Meilisearch data through a single API endpoint. It handles the entire Retrieval-Augmented Generation (RAG) workflow, from understanding the query to generating the response, automatically.

Here's what you get:

**Conversational AI for search** – Users ask natural-language questions and get precise, contextual answers from your Meilisearch index.

**Built-in RAG capabilities** – Retrieves relevant information, contextualizes it in your factual data, and generates accurate responses. No extra vector database, reranker, or separate AI service required.

**Native integration** – Runs directly on your Meilisearch instance with a single, OpenAI-compatible endpoint.

Less infrastructure. Faster implementation. No compromise on quality or accuracy.

## Why this matters for your business

**Get to market faster**

Meilisearch Chat turns months of AI integration work into days. You can prototype conversational experiences immediately, test them with real users, and iterate quickly without needing a dedicated AI team.

**Lower costs, less complexity**

We've streamlined the entire RAG pipeline, so you don't need separate LLM orchestration, reranking logic, or vector storage. Fewer moving parts means lower maintenance costs and less things that break.

**Better user experiences**

Whether it's customer support or internal knowledge bases, your users get direct, reliable answers in context – not just a list of links. That means faster resolution times, higher engagement, and happier users.

## Built for real-world use cases

**Retail & e-commerce** – Help shoppers find exactly what they need through natural conversation.

**Customer support** – Let users ask questions in plain language and get answers directly from your documentation or help center.

**Internal knowledge bases** – Turn your company documentation into a chat assistant that answers questions instantly.

**Developer docs** – Add conversational search to your API or SDK documentation so developers can find what they need faster.

## Simple to implement, easy to scale

If you're already using Meilisearch, adding conversational capabilities is straightforward. The `/chat` endpoint is [OpenAI-compatible](https://www.meilisearch.com/docs/learn/chat/getting_started_with_chat#configure-a-chat-completions-workspace), which means it works with your existing LLM setup and fits right into your current workflows.

You can start with your existing indexes – no need to rebuild or re-embed your data.

## A new approach to AI-powered search

With Meilisearch Chat, we're making it simpler to bring conversational AI to your data. Instead of managing multiple services and complex pipelines, you get a complete conversational search experience through one endpoint.

This isn't just another feature – it's a new foundation for more natural, human-like interactions with your data.

**Ready to make your search conversational? Explore the [docs](https://www.meilisearch.com/docs/learn/chat/getting_started_with_chat) for how to get started and [try /chat today](https://www.meilisearch.com/cloud)**

Introducing Meilisearch Chat: conversational search for your data

When building AI systems, one of the most significant decisions to make is how your model accesses and uses knowledge. Do you need real-time data every time, or can you rely on pre-stored, cached information? 

This is where the difference between retrieval-augmented generation (RAG) and cache-augmented generation (CAG) lies: 

* RAG retrieves live and updated data, making it ideal for situations where changes occur quickly and accuracy is crucial.
* CAG relies on cached information, delivering answers faster and more cheaply. It works well for repeated or predictable questions.
* RAG provides fresh, reliable responses and can handle various queries. However, it can be slow, expensive, and requires a solid infrastructure to run smoothly.
* CAG is quick, efficient, and easy to scale, but it can get outdated. It is also limited to what is already stored and requires regular updates to stay useful.

Choosing between the two comes down to considering your users: 

* Are the questions predictable or wide-ranging? 
* How up-to-date does the information need to be?
* What is your budget and infrastructure like?

You do not always have to pick just one. Hybrid approaches can combine both RAG and CAG. They allow the speed of cached answers while still retrieving live data when needed.

By the end of this article, you will understand how RAG and CAG work and which one better suits your projects.

## What is RAG (Retrieval-Augmented Generation)?

[RAG is a technique](https://www.meilisearch.com/blog/what-is-rag) that enhances the capabilities of generative AI by using external knowledge to provide more accurate responses to user queries. 

Rather than solely relying on the datasets the AI model was trained on, RAG searches a vector database or docs, retrieves the most relevant information, and then uses it to generate a response. This makes the answers more accurate and up-to-date. 

## What is CAG (Cache-Augmented Generation)?

CAG builds responses around the specific context window you provide. 

Unlike RAG, which searches external databases for information, CAG uses past interactions to shape its answers. This makes its output feel more relevant and personalized. 

In short, CAG makes AI less generic and more tuned to your needs by getting the proper context before generating a response.

## How does RAG work in AI models?

RAG combines two steps: retrieving information and generating a response. 

When you ask a question, the model first searches through an external knowledge base to find the most relevant documents. This retrieved information acts like notes or references that the large language model (LLM) keeps on hand for response generation. 

![A graphic depicting how RAG works.](https://unable-actionable-car.media.strapiapp.com/How_RAG_Works_8e6de82c99.png)

When generating its response, the LLM uses both internal knowledge obtained during training and the newly retrieved relevant context to return a response. It makes the response sound natural and grounded in real data. 

RAG co/mbines the best of both search engines and language models, ensuring the AI system is less prone to hallucinations.

Now, let’s take a closer look at CAG and how it works.

## How does CAG function in AI systems?

CAG works by providing AI with the necessary background information to generate an accurate response. When you interact with a CAG-enabled system, it does not rely on general knowledge alone. 

Instead, it takes note of the context you provide, such as your past conversations or personal preferences. This information is cached temporarily using a key-value (KV) cache and can be referenced while generating the responses.

![How CAG Works.png](https://unable-actionable-car.media.strapiapp.com/How_CAG_Works_8f5381b703.png)

During the generation process, the LLM combines this cached context with its pre-trained knowledge to produce personalized and relevant answers. 

Fundamentally, this limits the possibility of generating generic responses. CAG also continuously updates its context and determines the most relevant piece, ensuring the conversation stays aligned with your needs. 

## What is the main difference between RAG and CAG?

The main difference between these two [systems](https://www.meilisearch.com/blog/rag-types) is how they handle information when generating responses. 

RAG obtains external resources for knowledge augmentation, whereas CAG relies on previously stored data. 

Below is a quick preview of their key differences: 

![RAG vs CAG.png](https://unable-actionable-car.media.strapiapp.com/RAG_vs_CAG_cbdc135b87.png)

* **Data source:** RAG retrieves fresh information from external databases or search engines whenever a query is made. CAG works with pre-cached data that is already stored and ready for reuse.
* **Latency & performance:** RAG tends to have higher latency, as it requires a retrieval step every time, whereas CAG achieves low latency through preloading and reuse.
* **Cost efficiency:** With RAG, costs are typically higher due to the constant need to retrieve and process data. CAG, on the other hand, is more cost-effective since it eliminates repeated queries by reusing stored data.
* **Use case fit:** RAG is best suited for scenarios where real-time accuracy is essential. CAG is more useful for repetitive tasks where the same knowledge is applied often.
* **Scalability & maintenance:** RAG can be harder to maintain because it relies heavily on live updates and connections to external sources. CAG is easier to scale since cached data can be refreshed periodically and managed with less effort. 

Now, let’s look at the pros and cons of both RAG and CAG.

## What are the pros and cons of RAG?

RAG is beneficial for situations where accuracy is crucial, but it also has some limitations. Here are some upsides and downsides of RAG:

**Advantages**

* **Provides fresh information through real-time retrieval:** It can pull in the most up-to-date information, which is particularly beneficial when details change a lot.
* **Has a broad knowledge base:** Because it connects to external sources, it has access to a broader pool of knowledge than an AI model on its own.
* **Reliable for fact-checking:** It is beneficial for fact-checking or knowledge tasks where precision is key.

**Disadvantages**

* **Slower performance:** Since RAG must fetch new data, it has a higher inference time and responds more slowly.
* **Higher costs:** The constant retrieval of information makes it more expensive to operate.
* **Maintenance challenges:** RAG relies on external systems, so the results can be inaccurate if a source goes down or changes its data.
* **Complex setup:** Setting RAG up and keeping it running smoothly can be technically demanding.


## What are the pros and cons of CAG?

CAG may be faster and cheaper, but it does not come without downsides. Here are some advantages and disadvantages of CAG:

**Advantages**

* **Faster responses:** Since the data is pre-cached, there is no need to fetch information repeatedly, resulting in faster reactions.
* **Cost-effective:** CAG is more cost-effective and efficient because cached material reduces the need for repeated processing.
* **Efficient for repeat tasks:** CAG is ideal for repetitive queries, where the same information is frequently required.
* **Easier to scale:** It is easier to scale and maintain, as updating the cache can be done periodically with minimal hassle.

**Disadvantages**

* **Needs refresh cycles:** To remain useful, the caches used in CAG require periodic reprocessing and refreshment.
* **Risk of outdated information:** Cached data can go stale, meaning it might miss recent changes or updates.
* **Limited extended context:** The CAG is limited in terms of what is already stored, which means the scope of answers is narrower.
* **Potential bias:** If the cached content is biased or incomplete, the responses will reflect those flaws.

Wondering when it is best to use RAG over CAG? Let’s discuss the best-fit scenarios for both.

## When should you use RAG instead of CAG?

RAG is the better choice when your [retriever](https://www.meilisearch.com/blog/rag-tools) must fetch new information from an external source for every user’s query. 

For instance, in financial market updates, things move fast. Caching may not be helpful, as you do not want your AI system to return stale information. In such a case, RAG is an excellent approach. 

It can also be used in fact-checking scenarios or high-stakes research projects where credibility depends on having the most current and comprehensive information available. 

Another good use case of RAG over CAG is in scenarios where you do not expect repeated questions from users. It could also be used when you cannot precisely predict what users will ask. 

In these cases, caching will not be particularly helpful because the LLM must retrieve new information from the knowledge sources each time. 

In short, use RAG when your goal is to get current and accurate results, even if it takes a little longer or costs more.

## When is CAG preferable to RAG?

Use CAG when optimization for speed and low latency is key. By reusing embeddings and cached context, CAG reduces costs and improves response times for predictable scenarios.

This is particularly useful in situations such as customer support chatbots, where people frequently ask the same questions repeatedly. Answers to questions such as ‘How do I change my password?’ or ‘What is the refund policy?’ rarely change, so caching saves time and cost without sacrificing accuracy.

CAG also works well for internal knowledge bases where employees frequently need the same set of guidelines or instructions. 

Another great fit for CAG is large-scale deployments where keeping retrieval costs low is essential for scalability. 

While CAG may not always reflect the latest updates, its strengths lie in consistency and speed. So, if the information you are dealing with does not change often, then CAG is a practical choice.

## How do you choose between RAG and CAG for your project?

Choosing between RAG and CAG really depends on what your project needs most in terms of freshness, speed, or efficiency. 

Here are some key criteria to consider:

* **Nature of queries:** If your system faces unpredictable queries, RAG is stronger since it can reach out for fresh information. If the questions are repetitive (for example, common FAQs) then CAG will serve you better.
* **Budget considerations:** RAG is more costly because every query must undergo the retrieval phase. On the other hand, CAG is cheaper because the cached data is reused.
* **Performance requirements:** RAG can be slower, but it gives you depth and updated context. CAG is faster because it does not need to fetch new data each time it is executed.
* **Freshness of information required:** When working on tasks that require real-time information updates, such as financial data, it is recommended to use RAG. For stable knowledge, cached responses are more than enough.
* **Growth potential:** Scaling RAG can become complicated due to constant retrievals. Scaling CAG is easier, since updating caches is simpler to manage.
* **Handling varying outputs:** CAG keeps things quick and efficient when you do not need much flexibility, but when accuracy is non-negotiable, using RAG is the better option.
* **Technical setup:** RAG needs a stronger infrastructure to maintain live connections. CAG is easier to deploy due to fewer moving parts.

## Can RAG and CAG be combined?

Yes, you can combine RAG and CAG. In fact, combining RAG and CAG often gives you the best of both worlds. These are called [hybrid approaches](https://www.meilisearch.com/blog/rag-techniques). 

In such RAG pipelines, KV cache data is served first, and if it is missing or inadequate, the system triggers the retrieval step.

RAG and CAG can be combined in different ways, depending on your project’s goals. For example, you can build a:

* **Fallback hybrid:** The system defaults to cached responses for speed, but if the cache does not have a good answer, it switches to RAG for live retrieval.
* **Selective hybrid:** Certain types of queries are always routed to RAG (e.g., time-sensitive information) while routine or repetitive queries go straight to CAG.
* **Blended hybrid:** Both RAG and CAG are used together. Cached data provides a quick first draft, and RAG fills the gaps with fresh context if needed.
* **Tiered hybrid:** Responses start with cache for instant speed, then RAG silently updates or corrects the answer in the background so the answer to the query is fresher.

The benefits of a hybrid system are clear: faster responses, reduced costs, and still having the option to retrieve real-time data when needed. 

However, combining both also brings challenges. You need to decide on the best way to determine when to use the cache and when to fetch new information. Managing two approaches together can also add complexity to the system’s infrastructure and maintenance.

## RAG vs CAG: Choosing the right path for smarter, faster AI

When it comes to building more intelligent AI systems, RAG gives you freshness and accuracy by retrieving live data, while CAG provides you with speed and efficiency with its cached responses. 

The real power lies in knowing when to use each one or even combine them in a hybrid system. 

By balancing your API design, workflows, and knowledge integration needs, you can create LLMs that deliver optimized, real-world performance.

RAG vs. CAG: The Smarter Choice for Your AI Stack

Retrieval-augmented generation (RAG) has rapidly become one of the most effective methods for building AI systems. While most apps lean on Python, Ruby offers a lightweight path to implementing RAG pipelines without the need for large frameworks.

This guide will walk you through the essentials, from understanding what RAG means to building a working Ruby app.

You’ll discover: 

* What RAG is and how it fits into Ruby projects without heavy frameworks.
* Why developers pick Ruby for flexible, lightweight RAG setups.
* Libraries and services that make retrieval and generation work smoothly.
* Clear steps to build a working RAG app using plain Ruby.
* The difference between Ruby’s ecosystem and performance with Python for RAG development.
* How tools like Meilisearch help you build fast and reliable retrieval layers.

By the end, you’ll have everything you need to develop a functional RAG pipeline in Ruby from scratch.

## What is a RAG application in Ruby?

A RAG application in Ruby combines retrieval, augmentation, and generation to create smart, context-aware LLM responses. For details on how it works, you can take a peek at[ our guide on what RAG is](https://www.meilisearch.com/blog/what-is-rag). 

In essence, RAG retrieves relevant information from a vector knowledge base, augments a prompt with that data, and then sends it to a large language model (LLM) to generate accurate answers.

![How RAG Works.png](https://unable-actionable-car.media.strapiapp.com/How_RAG_Works_7352c5302f.png)

A frequent question is whether you can implement RAG without relying on heavy frameworks. Yes, you can. 

Ruby developers can rely on embeddings, search engines, and API calls to connect with LLM services for seamless integration. As a result, the AI-powered tools built through Ruby are incredibly lightweight. 

The following section explains why Ruby is a suitable option for using RAG.

## Why use RAG with Ruby?

In a RAG system, Ruby’s clean syntax and expressive language makes retrieval and embedding management relatively easy. Plus, you can connect to LLM APIs without any heavy frameworks. 

While the focus of this guide isn’t on Rails, generally, many teams already run production systems on Ruby or Rails. Adding RAG capabilities on top of that lets them integrate vector search and generation directly into existing apps without migrating their stack. 

Ruby’s ecosystem, although not as mature as Python's, simplifies API calls and background job handling, allowing developers to build lightweight scripts that optimize retrieval, augmentation, and generation.

Next, let’s look at the essential tools you’ll need to build a RAG application in Ruby.

## What tools are needed for RAG in Ruby?

To build a RAG application in Ruby, you need a combination of libraries and APIs that cover retrieval, embedding generation, and LLM integration. 

While the Ruby ecosystem is still growing, several reliable options are available to set up a complete RAG pipeline, many of which overlap with those discussed in [our RAG tools comparison](https://www.meilisearch.com/blog/rag-tools). 

Let’s look at the best ones below:

* **Langchainrb:** A Ruby library that aids in building retrieval and generation workflows through building blocks. It excels at supporting document loading, embedding generation, and basic orchestration.
* **Qdrant-ruby:** A popular open-source vector database as well as a Ruby client for Qdrant. It allows you to store and search embeddings efficiently from Ruby code.
* **Hugging Face or Transformer APIs:** These APIs can be called from Ruby to generate embeddings or run models remotely. They’re useful when you want to avoid maintaining your own ML infrastructure.
* **Other vector store gems:** Some developers use custom wrappers for databases, like PostgreSQL with pgvector, or they integrate with Meilisearch for hybrid keyword and [vector search](https://www.meilisearch.com/blog/what-is-vector-search).
* **LLM API gems:** These are gems for OpenAI, Anthropic, or other model providers that allow you to generate responses directly from Ruby.

There you have it: a set of tools associated with Ruby that provide the foundation for handling data retrieval and connecting to powerful language models.


## Step-by-step guide to building a RAG app in Ruby

This section is your walkthrough of a five-step process that covers data preparation, embedding generation, retrieval setup with Meilisearch, connecting to an LLM, and building a minimal interface to tie it all together.


### 1. Load and prepare your data

The first step is to bring text into your Ruby environment in a structured way. This could be from local files, APIs, or databases. 

You will need to clean the text, split it into logical chunks, and attach metadata (such as titles or URLs) so that retrieval later on is meaningful.


```ruby
# Example: Loading and cleaning a text file
file_path = "docs/guide.txt"
content = File.read(file_path).strip
chunks = content.split(/\n\n+/) # split into paragraphs
docs = chunks.map.with_index do |chunk, i|
  { id: i, text: chunk, metadata: { source: file_path } }
end
```


This preparation is the foundation that ensures your retriever can quickly find relevant information without having to parse messy inputs later.


### 2. Generate embeddings and store them

Once the documents are ready, you’re going to convert each chunk into a numerical embedding. You can use an API like OpenAI or Hugging Face for this purpose. 

Next, you will store the resulting vectors alongside the original text in a lightweight database or pair them with Meilisearch for hybrid retrieval.


```ruby
require 'net/http'
require 'json'

def embed(text)
  uri = URI("https://api.openai.com/v1/embeddings")
  headers = { "Content-Type" => "application/json", "Authorization" => "Bearer #{ENV['OPENAI_API_KEY']}" }
  body = { input: text, model: "text-embedding-3-small" }.to_json
  JSON.parse(Net::HTTP.post(uri, body, headers).body)["data"].first["embedding"]
end

embeddings = docs.map { |d| d.merge(embedding: embed(d[:text])) }
```


This gives your pipeline a semantic backbone for accurate retrieval.


### 3. Implement a retriever with Meilisearch

Now, it’s time to index your documents in Meilisearch. Why? To enable fast keyword and hybrid search. Feel free to store metadata alongside text for filtering as well. This gives you typo tolerance, relevance ranking, and blazing-fast queries.


```ruby
require 'meilisearch'

client = MeiliSearch::Client.new('http://127.0.0.1:7700', 'masterKey')
index = client.create_index('documents', { primaryKey: 'id' })
index.add_documents(docs)

# Query example
results = index.search('embedding generation')["hits"]
```


Developers often underestimate how significantly Meilisearch outperforms manual filtering or SQL full-text search in text retrieval. A clean index makes every later step more reliable.


### 4. Pass retrieved context to an LLM

Now that you have the retrieved context, let’s move it on to the LLM so the answers are actually grounded in data. You’ll build a prompt that includes the essentials, such as user query, brief instructions, and a trimmed context window. 

You can avoid the bloat by focusing on only the most relevant snippets. If you really want to show where the facts came from, just use citation markers. 


```ruby
require "openai"

class AnswerGenerator
  def initialize(model: ENV.fetch("OPENAI_MODEL", "gpt-4o-mini"))
    @client = OpenAI::Client.new(access_token: ENV.fetch("OPENAI_API_KEY"))
    @model  = model
  end

  def answer(query:, hits:)
    context = build_context(hits, max_chars: 6_000) # keep under model limits
    messages = [
      { role: "system", content: "You are a helpful assistant. Answer using only the provided context. Cite sources like [doc:index]. If the answer is not in the context, say you do not know." },
      { role: "user",   content: "Query: #{query}\n\nContext:\n#{context}\n\nAnswer:" }
    ]

    @client.chat(parameters: { model: @model, messages: messages, temperature: 0.2 })
           .dig("choices", 0, "message", "content")
  end

  private

  def build_context(hits, max_chars:)
    buf = +""
    hits.each_with_index do |hit, i|
      snippet = hit.dig("_formatted", "content") || hit["content"] || hit["snippet"] || ""
      block = "[doc:#{i+1}] #{snippet}\n"
      break if buf.size + block.size > max_chars
      buf << block
    end
    buf
  end
end
```


Several factors enhance the retrieval-to-generation step. 

Keep the temperature low so the model sticks to facts. If the context doesn’t support an answer, it should say so clearly. Trim or rerank longer contexts to stay within token limits. 

And don’t forget to log document IDs for easy traceability and later evaluation.


### 5. Build a CLI or service interface

Here’s a tidbit you’ll enjoy: wrap retrieval and generation in a small interface so you and your teammates can try the flow quickly. 

A CLI keeps things dependency-light and works well for scripts, cron jobs, and demos. Add retries and basic timeouts to handle flaky networks or rate limits.


```ruby
require "json"
require "timeout"
require_relative "indexer"          # your Meili client and search method
require_relative "answer_generator" # the class above

class RagCli
  def initialize
    @indexer  = Indexer.new(index: ENV.fetch("MEILI_INDEX", "docs"))
    @llm      = AnswerGenerator.new
  end

  def run
    puts "RAG on Ruby. Type a question, or 'exit'."
    loop do
      print "> "
      q = STDIN.gets&.strip
      break if q.nil? || q.downcase == "exit"
      next if q.empty?

      hits = safe { @indexer.search(q, limit: 5) } || []
      if hits.empty?
        puts "No context found."
        next
      end

      response = safe { @llm.answer(query: q, hits: hits) }
      puts "\n#{response}\n\n"
      puts "Sources:"
      hits.each_with_index do |h, i|
        id = h["id"] || h.dig("_formatted", "id") || "unknown"
        puts "  [doc:#{i+1}] #{id}"
      end
      puts
    end
  end

  private

  def safe(max_retries: 3)
    tries = 0
    begin
      Timeout.timeout(30) { return yield }
    rescue => e
      tries += 1
      sleep(0.5 * tries)
      retry if tries < max_retries
      warn "Error: #{e.class} #{e.message}"
      nil
    end
  end
end

RagCli.new.run if $PROGRAM_NAME == __FILE__
```


A few production checks go a long way. Ensure API keys are validated upfront and with clear errors when any information is missing. If your provider allows it, stream tokens so users see answers as they’re generated. 

Log the essentials, such as queries, selected document IDs, answers, and citations, for easy debugging and evaluation later. Keep prompts within safe length limits and sanitize inputs to block prompt injection attempts. 

And if you’re exposing the app over HTTP, run it through a small Rack or Sinatra service with sensible timeouts and rate limits to maintain stability.

This completes the core loop: retrieve with Meilisearch, ground the answer with your context, and ship an interface that is easy to run and extend.

## How is RAG in Ruby different from Python?

While Python dominates the RAG ecosystem, Ruby has its own strengths for developers who prefer a leaner, language-level approach. 

The key differences lie in the ecosystem, available tools, and performance characteristics.

![Ruby vs. Python for RAG.png](https://unable-actionable-car.media.strapiapp.com/Ruby_vs_Python_for_RAG_4790deaa9a.png)

Ruby focuses on simplicity and direct integrations rather than building entire RAG stacks internally. 

Python offers more built-in support for machine learning and orchestration, but can feel heavier for smaller apps. 

The right choice depends on your goals, scale, and existing stack.

## Unlocking the power of RAG with Ruby

RAG unlocks new possibilities for Ruby developers who want intelligent, context-aware applications without shifting their entire stack. 

For those working with Rails, our [guide to building RAG apps on Rails](https://www.meilisearch.com/blog/build-rag-apps-on-rails) shows how to extend these ideas further. 

By combining Ruby’s flexibility with the right tools, you can build retrieval and generation pipelines that are both lightweight and powerful.

How to Build RAG Applications Using Ruby: A Developer’s Guide

How to Build RAG Applications on Rails: Step-by-Step Guide

RAG (retrieval-augmented generation) evaluation is the process of measuring how well a system actually retrieves relevant information to generate accurate answers for the users. It helps improve retrieval logic as well as the response quality.

RAG evaluation is crucial for identifying gaps in the system’s performance and reliability across various use cases.

The most common methods associated with RAG evaluation are human evaluation, reference answer scoring, and retrieval relevance checks.

In addition to this, the most widely used retrieval metrics are Exact Match, F1 score, Recall, Precision, BLEU, and ROUGE. The tools you’ll see used most often in this domain include Meilisearch, LangChain, RAGatouille, and Haystack. 

Now that the stage is set, let’s have a deeper look at what RAG evaluation is and the methods involved.

## What is RAG evaluation?

RAG evaluation is the process that determines how effectively a [RAG system](https://www.meilisearch.com/blog/what-is-rag) retrieves information and then uses that information to generate accurate answers for users. 

You might have noticed that RAG evaluation covers both retrieval and generation. This is to provide a clear view of the system's overall correctness. 

Once you have that, it becomes easier to identify weaknesses, test improvements, and benchmark different models or approaches. 

## Why is RAG evaluation important?

RAG evaluation is important because it helps determine whether your system is actually delivering responses that are relevant and helpful to users. 

Without this kind of evaluation, it's difficult to know if your pipeline is improving or drifting off course.

RAG evaluation can help teams identify and address issues early. This can include spotting hallucinations, irrelevant retrieval, and complex, inaccurate answers: 

* **Reduces hallucination risk:** Evaluation highlights the generation based on weak retrieval, allowing you to correct it immediately.
* **Improves user trust:** This evaluation helps make your RAG system fact-based and transparent, improving user confidence.
* **Enables faster iteration:** With regular evaluation, you can measure impact across changes and optimize performance quickly.

Next, let’s look at the methods used in RAG evaluation. 

## What methods are used in RAG evaluation?

The different methods used in RAG evaluation are shown in the image below:

![RAG Evaluation Methodologies.png](https://unable-actionable-car.media.strapiapp.com/RAG_Evaluation_Methodologies_28942e88c7.png)

These approaches evaluate both the retriever and generator, ensuring no compromise on high-quality outputs at any step.


### End-to-end evaluation

First up is the end-to-end evaluation. It focuses on the overall output of the RAG system. 

The factors under consideration are the usefulness, accuracy, and fluency of the generated response without breaking down the pipeline components. 

While it provides a quick way to compare systems, it doesn’t reveal which part of the pipeline – if any – actually failed.

### Retriever evaluation

Retrieval evaluation, as its name suggests, focuses on whether the retrieval system brings back relevant documents for the query. 

The evaluators can be human or automated. Either way, their function is to judge the quality of the retrieved context using a relevance score. 

You can pinpoint the weakness that led to poor answers downstream. 

### Grounding evaluation

Grounding eval checks if the generated response remains faithful to the retrieved content. 

How do we find that out? The output is compared with the supporting context to check factual accuracy. Naturally, this helps you spot and fix potential hallucinations.


## What are the key RAG evaluation metrics?

RAG evaluation metrics measure the quality of the retrieved documents and the accuracy of the generated response. Developers can use this information to identify where the pipeline is failing and make the necessary corrections.

Some of the key RAG evaluation metrics to know are the following:

* **Precision and recall:** Precision measures the percentage of retrieved docs that are actually relevant, while recall assesses the percentage of relevant documents that were retrieved. You need both to achieve a balance between accuracy and coverage in information retrieval. 

![Precision and recall.png](https://unable-actionable-car.media.strapiapp.com/Precision_and_recall_13f62b830c.png)

* **Groundedness:** This metric evaluates whether the generated output is factually consistent with the retrieved documents. A high groundedness score means the model is less likely to hallucinate.
* **Faithfulness:** Faithfulness measures how well the generated text sticks to the facts in the source material. Unlike groundedness, faithfulness seeks to identify distortions or unsupported claims.
* **Answer relevance:** Is the final answer even relevant to the user’s query? This is determined through answer relevance that relies on human ratings or automated scoring methods.
* **Fluency:** Fluency assesses how natural and readable the generated response is. After all, the target user is human. While secondary to accuracy, fluency impacts user trust and the overall experience.

Together, these metrics serve as essential benchmarks for any RAG applications, providing a complete and honest picture of the RAG system.

## What are the challenges in RAG evaluation?

RAG evaluation has a few unique challenges that we’ll list here:

1. **No universal benchmarks:** Unlike classic machine learning, there is no universal benchmark for RAG evaluation that applies to every [type of RAG system](https://www.meilisearch.com/blog/rag-types), as they all differ in architecture.
2. **Human evaluation costs too much:** Groundedness and relevance often require human judgment, which is expensive and hard to scale.
3. **Dynamic data and drift:** The frequent interaction of RAG pipelines with evolving knowledge bases means that evaluation results soon become outdated.
4. **Trade-offs between metrics:** There’s always a trade-off between the metrics. For instance, optimizing for fluency might reduce faithfulness. Similarly, improving recall could harm precision. 

## How is RAG evaluation implemented?

Different tools, frameworks, and services are responsible for implementing RAG evaluation. The evaluation process is mostly a combination of human review, automated evaluation, and continuous monitoring. 

Most teams prefer to conduct offline testing first and then move on to live monitoring once the pipeline is deployed. 

### Popular RAG evaluation frameworks

Popular open-source frameworks, such as Ragas, TruLens, and DeepEval, are a major help when it comes to automated testing. 

These frameworks use automated tests to help measure retrieval precision and factual consistency, and monitor hallucination rates. Through the LLM-as-a-judge grading they provide, you can easily identify weaknesses and correct them within the system.

For an easily digestible overview of retrieval layers and orchestration platforms, feel free to check out our guide on[ the top RAG tools](https://www.meilisearch.com/blog/rag-tools). 

### Synthetic test datasets

Another common method is creating synthetic evaluation datasets that replicate real-world query patterns. 

How does this help? It allows developers to benchmark retrieval quality and groundedness under a controlled set of conditions. You get to identify coverage gaps before the pipeline enters the production stage.

### Human-in-the-loop validation

There’s no alternative to human insight. Even with strong automation, human evaluation remains critical. 

Subject matter experts review sampled queries to verify if the retrieved content is relevant and the generated answers are accurate. It provides a nuanced view of the system that metrics alone cannot determine.

### Continuous monitoring in production

As a developer, you already know that evaluation does not stop once a system is live. Production monitoring tracks key metrics, including latency, error rates, and feedback loops. 

A reliable retrieval layer, such as Meilisearch, supports this process by consistently delivering fast, high-quality context that evaluation frameworks depend on.

Together, these approaches form a cycle of pre-deployment testing, human review, and real-time monitoring. As a result, the RAG pipelines remain accurate, efficient, and trustworthy over time.

## What are the best practices in RAG evaluation?

Best practices give you the most efficient way to ensure that pipelines are tested thoroughly and fairly. Early weakness detection, hallucination spotting, and maintaining reliability are all part of these best practices.

![Best Practices in RAG Evaluation.png](https://unable-actionable-car.media.strapiapp.com/Best_Practices_in_RAG_Evaluation_06aaf08217.png)

Some of the key best practices include:

* **Use more than one test set:** Evaluate your system with multiple data types and domains, rather than relying solely on a single benchmark. This exposes weaknesses that would otherwise go unnoticed.
* **Combine human and automated checks:** Humans provide context-sensitive judgments, while automated metrics offer speed and scale. A balance between both is your best bet.
* **Test the robustness of the system:** Introduce adversarial or noisy inputs to see how well the system maintains accuracy under less-than-ideal conditions.
* **Track longitudinal performance:** Don’t stop evaluating pipelines. Continue over a staggered time period. This helps identify model drift and measure whether improvements stick.
* **Target user expectations:** Design evaluations that mirror how end-users will interact with the system. Metrics should reflect usefulness, not just technical accuracy.

These practices make RAG evaluation a reliable part of development, ensuring systems remain stable and trustworthy at scale. To explore techniques that can further improve pipeline performance, take a look at our article on[ advanced RAG techniques](https://www.meilisearch.com/blog/rag-techniques).

## How long does it take to evaluate a RAG pipeline?

The answer to this question lies in the variables. Smaller prototypes can be evaluated within hours, while production-scale pipelines require days of rigorous evaluation. 

The duration also depends on factors such as dataset chunk size, evaluation methods, and the number of metrics being tracked. 

For example, using human annotators for quality checks is slower but provides better, more nuanced insights than automated benchmarks. 

The complexity of the pipeline also affects the timeline.

## Are there any papers on RAG evaluation?

Yes, there are several notable papers on RAG evaluation, and most of them can be found on arXiv. Here are some recent ones from 2024 and 2025:

* ‘Evaluation of Retrieval-Augmented Generation: A Survey’ (2024) by Hao Yu et al.
* ‘Evaluating Retrieval Quality in Retrieval-Augmented Generation’ (2024) by A. Salemi et al. 
* ‘VERA: Validation and Evaluation of Retrieval-Augmented Systems’ (August 2024) by Tianyu Ding et al.
* ‘Unanswerability Evaluation for Retrieval-Augmented Generation’ (December 2024) by Xiangyu Peng et al.
* ‘Retrieval Augmented Generation Evaluation in the Era of Large Language Models’ (April 2025) by Aoran Gan et al.
* ‘The Viability of Crowdsourcing for RAG Evaluation’ (April 2025) by Lukas Gienapp et al. 

These papers provide a rich foundation for anyone wanting to understand or benchmark RAG evaluation methods, metrics, and emerging best practices.

## The future belongs to teams that master RAG evaluation

RAG evaluation is not just a technical exercise – it is the feedback loop that keeps RAG systems honest, reliable, and ultimately useful. 

In practice, evaluation becomes the difference between a system that only looks impressive in demos and one that consistently delivers value in production.

As data sources shift, user expectations evolve, and generative models grow more complex, the ability to adapt evaluation strategies will separate cutting-edge teams from those left chasing errors downstream. 

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