Elasticsearch vs Qdrant (vs Meilisearch): Which search solution actually fits your needs in 2025?

This is the third in our series on practical RAG with Meilisearch. In [post #1](https://www.meilisearch.com/blog/mastering-rag), we explained how hybrid retrieval (lexical + semantic) boosts both recall and precision so your results are actually the right ones. In [post #2](https://www.meilisearch.com/blog/rag-with-meilisearch), we walked through the full RAG pipeline: query refinement → retrieval → generation, and why keeping those pieces integrated keeps latency low and the system simple.

In this post, we focus on how teams actually ship. We’ll look at three patterns that show up over and over in real products: **R**etrieval, **A**nswers, and **G**rowth, and illustrate each with concrete use cases from teams using Meilisearch today.

## (R)etrieval: making the search bar fast, precise, and dependable

Shoppers don’t want an essay about sneakers; they want the right product card with a clean **'Add to cart' button**. In a commercial flow, generation is optional, but **retrieval quality** is everything. That’s the heart of the revenue pattern: make rank #1 correct, obvious, and fast, so the next click is a purchase, and not a string of revised queries.

![retrieve-find-results-fast.png](https://unable-actionable-car.media.strapiapp.com/retrieve_find_results_fast_84dd0e3314.png "Retrieve: Find results fast and accurately")

You can see this play out at [**Bookshop.org**](https://www.meilisearch.com/blog/bookshop-increases-search-based-purchases), where better retrieval directly translates into more purchases from search, increasing from 14% of searches leading to a purchase to 20%, a **43%** relative lift. That’s the sort of metric you feel in the revenue graph, and it came from relevance, not more copy. 

[**Minipouce**](https://www.meilisearch.com/blog/minipouce-top-grade-customer-experience) showcases the “great on day one” angle: solid defaults, typo tolerance, and intuitive facets produce results that feel right without weeks of tuning. 

And for teams outside classic retail, [**Qogita**](https://www.meilisearch.com/blog/qogita-simplifies-b2b-trade) (B2B marketplace) and [**HitPay**](https://www.meilisearch.com/blog/hitpay-streamlines-search) (fintech/commerce) exhibit the same retrieval-first pattern, dropping into very different surfaces: fast to set up, easy to operate, and friendly to lean teams.

What makes this work is a short, disciplined loop: hybrid ranking to catch both exact matches (“Air Max 270 black 42”) and intent phrasing (“black Nike runners size 9”), facets that narrow instantly, and a UI that respects the action. Keep an eye on zero-results queries, measure search→purchase conversion, and treat synonyms/typos as first-class relevance features. When intent is commercial, **R > G**: retrieval first, retrieval last.

## (A)nswers (retrieval + generation): one reliable answer beats ten links

Internal questions are scavenger hunts across policies, procedures, docs, tickets, and code. Your teammate doesn’t want ten blue links; they want a short, sourced answer they can act on. That’s where **retrieval + generation** shines: retrieval assembles the right building blocks; generation assembles them into a trustworthy and concise answer.

![answer-produce-accurate-answers.png](https://unable-actionable-car.media.strapiapp.com/answer_produce_accurate_answers_c4524820d2.png "Answer: Produce accurate answers synthesizing the searched results.")

In practice, [**Louis Vuitton**](https://eu.louisvuitton.com/eng-e1/homepage) is a clear example: store associates get consistent, fast answers to policy and product questions across locations. The value isn’t that an LLM can chat – it’s that retrieval surfaces current, canonical guidance, and generation condenses it into steps a person can follow. 

For developer productivity, [**Symfony**](https://www.meilisearch.com/blog/symfonys-solution-for-documentation-search) shows what low-latency, well-scoped search looks like at scale: sub-10 ms responses over **85,000+** docs across **30** versions help engineers land on the exact class, command, or procedure they need. At [**OCTO Technology**](https://www.meilisearch.com/blog/meilisearch-is-a-partner-of-choice-for-octo), the same approach operates as a dependable, production-ready solution in client environments where reliability and maintainability are crucial.

The craft is in the details: make retrieval source-aware (favor canonical and current, down-rank duplicates and stale pages), retrieve **snippets** instead of whole pages so the model stays on a short leash, and use a prompt that’s opinionated about behavior, yet can synthesize briefly, cite every claim, include steps or a code block when useful, and say “no evidence found” instead of guessing. A tiny feedback loop (“useful / missing / outdated”) helps you fix the source, not just the surface.

## (G)rowth (infrastructure): scale you don’t have to think about

Most RAG stories focus on search and answers. That’s great, until the demo grows up. The catalog doubles, traffic spikes on a Tuesday, someone adds three new locales, and suddenly your tidy prototype is juggling a keyword engine, a vector store, and a re-ranker stitched together with custom middleware. Queries get slow, dashboards get noisy, and your on-call starts carrying two laptops.

![grow-scale-effortlessly.png](https://unable-actionable-car.media.strapiapp.com/grow_scale_effortlessly_0f7c99ddde.png "Grow: Scale effortlessly as the data and customer base grow")

Some things are meant to be boring, and **scaling** should be one of them. Instead of an ever-changing stack where you tweak three services every time the user base grows, **Meilisearch offers one hybrid path** that stays fast as you add data, users, and languages. **One engine and one request handle both lexical precision and semantic recall.** No service hopping, fewer moving parts, and fewer surprises. As the client, you focus on content and UX, and we focus on the plumbing.

That’s the experience we aim for with Meilisearch. Index growth feels like adding shelves, not rebuilding the warehouse. Versioned indexes and safe schema changes turn “we need a new attribute” into a routine deployment. Language-aware analyzers, combined with embeddings, handle phrasing differences without a meeting. Monitoring covers what actually matters: latency *and* quality. That way you can spot recall/precision drift before users do. And vectors don’t have to eat your budget: compact embeddings, ANN indexes, and only storing what moves relevance keep costs sensible.

You can see this pattern in the wild. **CarbonGraph** moved from a standalone vector setup and found that adding embeddings inside a hybrid engine was straightforward, meaning no extra choreography was required. 

[**Hugging Face**](https://www.meilisearch.com/blog/hugging-face-democratizes-ai) is the stress test: discovery keeps working across **2.2M model cards**, **500k datasets**, and **60k demos** without turning every query into a science project. 

And for “hard-mode” content, [**TutKit**](https://www.meilisearch.com/blog/tutkit-multilingual-learning-at-scale) serves **15,000+** items in **26 languages**, while [**Bildhistoria**](https://www.meilisearch.com/blog/powering-bildhistoria-discovery) keeps **90,000+** richly annotated historical images findable and fast. Different shapes of data, the same quiet promise: it scales, and you barely notice.

## Ready, Aim, Go!

RAG isn’t one architecture; it’s a set of patterns you can plug in and ship. With Meilisearch, you get three practical pillars to build on: **Retrieve**, **Answer**, and **Grow**. **Retrieve** for quick discovery. **Answer** for concise, sourced guidance. **Grow** for speed and stability at scale. Start where you are, keep one path, and keep iterating.

Retrieve and gain: Three RAG use-case patterns you can ship today

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

Elasticsearch vs Pinecone (vs Meilisearch): Which search solution actually fits your needs in 2025?

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

Algolia pricing guide: Is it worth the cost in October 2025?

Welcome to our new roundup of product updates, community news, and everything happening across the Meilisearch ecosystem.

This October was big: we wrapped up [**Launch Week**](https://www.meilisearch.com/launch-week-q3-2025?utm_campaign=238324931-Monthly%20Newsletter&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-8neQp3zIW5wCVhJGcT4mmJ79gYs9F6CC6SnoLDy1i_-B3FDbW6rum2QjF65Mqw7iaNbZSC), were featured in the Product Hunt top products newsletter, and hit a few other exciting milestones along the way. Let’s dive in 👇

---

## 🚀 Product updates: what’s new at Meilisearch

**Another [Launch Week](https://www.meilisearch.com/launch-week-q3-2025) is in the books!** From conversational AI and multi-modal search to personalization and a new pricing model - we shipped **five major updates in just five days**:

- [**Meilisearch Chat**](https://www.meilisearch.com/blog/meilisearch-chat)
- [**Advanced geosearch**](https://www.meilisearch.com/docs/learn/filtering_and_sorting/geosearch)
- [**Enhanced multi-modal search**](https://www.meilisearch.com/docs/learn/ai_powered_search/image_search_with_multimodal_embeddings)
- [**Search personalization**](https://www.meilisearch.com/docs/learn/personalization/search_personalization)
- [**Resource-based pricing**](https://www.meilisearch.com/blog/resource-based-pricing)

Explore the Launch Week:

🔗 [Explore each release, day by day.](https://www.meilisearch.com/launch-week-q3-2025)

🔗 [Launch Week recap blog](https://www.meilisearch.com/blog/launch-week-2).

---

### ⚡ AI Search experience, out of the box

We’ve made it easier than ever to experience the power of **AI Search** - no setup, no data required. With just one click, you can explore a built-in mock dataset and see real-time progress feedback as the search runs:

![](https://hs-25945010.f.hubspotemail-eu1.net/hub/25945010/hubfs/AI%20search%20mock%20data.gif?upscale=true&width=1120&upscale=true&name=AI%20search%20mock%20data.gif)

---

### ☁️ We are available on AWS marketplace

We’re thrilled to announce that **Meilisearch is now live on the [Amazon Web Services (AWS)](https://aws.amazon.com/marketplace/pp/prodview-7ougkqatzyr7i)** Marketplace! 😎 AWS has been an incredible partner, and this makes it even easier for customers to deploy Meilisearch directly.

👉 Learn more in this [blog post.](https://www.meilisearch.com/blog/aws-marketplace)

---

### 📊 Cloud UI - billing update

We’ve clarified how project metrics are displayed in the Cloud UI. This also includes users of free trial as well paid resource-based pricing model users:

- **Transparent project view:** project cards will now display **real usage data**, not your peak billing values.
- **More visibility:** both resource-based and free-trial projects will now show current **searches and documents** metrics directly in the dashboard.

![](https://hs-25945010.f.hubspotemail-eu1.net/hub/25945010/hubfs/image%20(47).png)

---

### 🚧 Coming soon: search analytics V2

We’ve heard your feedback and we're now in the final stretch before releasing an upgraded version of Search Analytics. Stay tuned for a deeper, more actionable view of your search performance.

---

## Community section & ecosystem news:

### Product Hunt feature:

**Meilisearch Chat** was featured in the [**Product Hunt Daily**](https://www.producthunt.com/newsletters/archive/44612-videos-that-don-t-suck) newsletter – reaching over a million readers! Huge thanks to our open-source community for the love and excitement around conversational AI built on your own data. 💜

### 🔥 Fireship x Meilisearch:

We were also featured in the latest [**Fireship**](https://www.youtube.com/watch) video covering **OpenAI’s** new browser, Atlas! If you haven’t tried Meilisearch yet, here’s your sign 👀 - there’s even a little code surprise in the video.

→ Watch the video [here](https://www.youtube.com/watch?v=5uSboan45Zg).

### 💡 Customer highlights:

See what users are saying about Meilisearch:

*"Since we redesigned our Program Finder and implemented Meilisearch, our conversions have more than doubled. We are so very happy with Meilisearch."*

Gabriela Sa Teles, Assistant Director of Digital Services at the **University of Missouri-Kansas City**

## Tips & resources:

**We published a new guide:** [Optimize Indexing Performance with Batch Statistics](https://www.meilisearch.com/docs/learn/indexing/optimize_indexing_performance)

This guide serves to help you use *progressTrace* to analyze and resolve indexing bottlenecks in Meilisearch.

---

## 🏢 Company news

In October, we held our regular company offsite where we used the time to refine our long-term vision. During the offsite, we also built this multimodal demo on a massive dataset:

👉 [check it our here.](https://flickr.meilisearch.com/)

Our CEO Quentin also wrote a [post](http://linkedin.com/posts/quentin-de-quelen-4241a865_we-ran-a-small-experiment-during-our-offsite-activity-7392154072931389441-z62H) describing the entire stack, if you'd like to learn more about it.

💼 **We’re hiring!**

Meilisearch is expanding, and we’re opening  several new roles across teams, with more to come!

If you know someone who’d be a great fit - or if you’re curious about what’s next for us 👉 keep an eye on our [**Careers page.**](https://www.meilisearch.com/careers)

![45C6AD8A-89DA-49B6-81F3-7C109EB6B6EF_1_102_o.jpeg.webp](https://unable-actionable-car.media.strapiapp.com/45_C6_AD_8_A_89_DA_49_B6_81_F3_7_C109_EB_6_B6_EF_1_102_o_jpeg_326f23623c.webp)

--- 

Receive this updates in your inbox by subscribing to our [monthly newsletter](https://dub.sh/meili-newsletter). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/) and participate in our [Product Discussions](https://github.com/orgs/meilisearch/discussions).

For anything else, join our developers community on [Discord](https://dub.sh/meili-discord).

Meilisearch October updates

Most CTOs assume that implementing search is a quick, done-in-a-sprint kind of task. Something their dev team can figure out in a week. 

But the truth couldn’t be more different. According to our survey, more than half (55%) of development teams require **three weeks or more** to roll out a search solution. 

And they are the lucky ones. For some, it can take **two or more months**. 

This begs the question: **why is something as vital as search still so hard to get right?**

We asked over 100 CTOs and technical CEOs to share what went wrong when implementing search and what they wish vendors would do differently. Their answer reveals common pitfalls and insights that can help you avoid the same costly mistakes. 

Let’s dive into what they shared and how it applies to your own search initiatives. 

## Key insights (TL;DR)

* 86% of respondents called search **‘very important’ and ‘critical’ to their product’s user experience** across industries like e-commerce, fintech, healthtech, edtech, etc. 
* 55% of development teams spent **three or more weeks** implementing search. 17% struggled for **two months or longer**. 
* The biggest blockers aren’t just bugs – they’re structural. The top three: **inflexible tools (61%), broken documentation (47%), and a lack of expertise (34%).**
* **Less than 1%** of respondents said integrating search was easy for them.
* During the implementation phase, the majority of teams turned to **vendor documentation (55%)** and **support and customer success teams (49%)** for help, while **20%** relied on AI assistants like **ChatGPT**.
* **Fast response time (71%), accurate results (51%)**, and the **ability to handle typos or fuzzy matching (50%)** are the top three features teams expect in their search experiences.

## Insight #1: Most teams spent weeks – or months – on search implementation

According to our survey, only 15% of teams implemented search in under a week, while **55% took more than three weeks** to do so. 

For 17% of development teams, the process stretched to **at least two months**, resulting in a significant amount of wasted time. 

![How long did it take to integrate the search functionality_.png](https://unable-actionable-car.media.strapiapp.com/How_long_did_it_take_to_integrate_the_search_functionality_1d72bf913b.png)

These results reflect a consistent trend: integrating search has become a slow, resource-draining process for most teams. 

In the open-ended answer section of our survey, one respondent even said, “Plan for double the time to implement.” 

Users expect each search experience to be fast, accurate, and typo-tolerant by default. Giants like Google and Amazon have set high standards. Yet behind the scenes, devs are investing massive amounts of time just to get the basics working.

From our perspective at Meilisearch, that’s a costly baseline. 

## Insight #2: Search fails at the structural level, not just in the code

**Less than 1%** of respondents said that their search integration went smoothly.

When asked what slowed them down, they highlighted three most common issues: 

1. **Technical limitations (61%):** Inflexible infrastructure, clunky APIs, or the wrong tools often force teams to rethink and rebuild key systems. 
2. **Poor documentation (47%):** Missing or outdated documentation adds friction and guesswork into the process.
3. **Lack of expertise (34%):** Many teams don’t have the experience to successfully implement search, leading to false starts and much trial and error. 

![What were the biggest challenges your team faced during the search integration process.png](https://unable-actionable-car.media.strapiapp.com/What_were_the_biggest_challenges_your_team_faced_during_the_search_integration_process_f458e8105f.png)

While bugs and integration hiccups can cause short-term delays, the real problem comes from **structural issues**. 

This means the architecture of the product wasn’t built with search in mind, the documentation is more of a hindrance than a help, and teams lack key skill sets to implement search properly. 

The result is search that ships late, costs more, and becomes harder to maintain with every iteration.

## Insight #3: When teams need help, the docs don’t always deliver

When search implementation gets tough – as it often does – what do teams do? They look for support. 

![What resources did you rely on for integrating search.png](https://unable-actionable-car.media.strapiapp.com/What_resources_did_you_rely_on_for_integrating_search_75df6f93ba.png)

The **most common first stop is vendor documentation (55%)**, which ironically is the second biggest roadblock to implementing search, as noted in our previous insight. 

Often, documentation is incomplete, outdated, or simply hard to navigate. 

So, if the most relied-on source of guidance is also a leading pain point, it’s no wonder people get stuck for weeks. 

Respondents also relied on vendor support or customer success teams (49%), online tutorials (48%), and peers or communities (e.g., Discord) (45%).

Almost one in five developer teams (19%) turned to AI tools, such as ChatGPT or Copilot, for help – a clear sign that AI tools are becoming an integral part of the troubleshooting stack. 

Our survey findings show that **support is fragmented.** Developers chase answers across multiple sources, wasting time and energy as deadlines rapidly approach.

## Insight #4: What developers really want from their search tools

Aside from the challenges, our team also asked technical leaders what they wished their search tools had done better. 

Their answers focused on ways to save time, reduce confusion, and avoid frustration. 

![A graphic listing the most common responses from developers working on integrating a search functionality into their products.](https://unable-actionable-car.media.strapiapp.com/What_developers_want_from_their_tools_489451aa44.png)

### Better, clearer documentation 

Teams want docs that go beyond the basics, with real-world examples and easy-to-follow setup steps to speed up integration. 


### Simpler, unified APIs

A common frustration is having to integrate multiple endpoints, so leaders are requesting unified APIs. 

“*Provide a single, well-documented API that can pull from multiple sources (documents, emails, chats, etc.) with consistent formatting,*” one respondent said.  


### Seamless integration tooling

Teams want builders that work with popular frameworks and data pipelines, without spending too many hours on setup. 

“*Make sure you find a platform that integrates well with other software development *[tools],” was a recurring sentiment among respondents. 


### Flexible customization and metadata control

Search isn’t a one-size-fits-all, so teams want more control over their ranking, filtering, and metadata display. 

One respondent said: “*Offering customizable filters and permission controls would help teams tailor search to their specific workflows.*” 

Respondents aren’t asking for the impossible. The idea is to make search implementation more straightforward, especially for teams working in fast-paced industries. 

That’s why, at Meilisearch, we continually update our documentation, prioritizing a unified and straightforward API that anyone can understand. 


## Survey methodology and limitations

Our survey was conducted in July 2025 using SurveyMonkey’s paid Audience panel. 

Respondents were decision-makers – CTOs, technical founders, or equivalent roles – at US-based software companies across all industries and company sizes. 

To qualify, respondents had to pass two screening questions: one to confirm their technical leadership role and another to verify their experience with implementing search functionality. 

This ensured we got answers from those with practical experience on the subject.

The survey included 11 questions in total (2 screening, 9 core), and we received 119 qualified responses. You can find complete data in [this spreadsheet](https://docs.google.com/spreadsheets/d/1Q1Z0brmX1AilNnbW5PCORSqUCCoAXGyN7TFZbLuSGvU/edit?gid=1781222527#gid=1781222527).

As with any data study, there are constraints. Responses may not fully represent large enterprises or heavily regulated industries, where search implementation challenges may differ.

Furthermore, the respondents were compensated by the SurveyMonkey platform for their participation, which may have introduced bias. 

Finally, our mix of multiple-choice and open-ended questions provided quantitative and qualitative data, but may not have captured every nuance of the implementation process. 


## Search doesn’t have to be hard

The data paints a clear picture: search is needlessly complicated. Most teams spend weeks – even months – just to set up the basics, bogged down with technical hurdles and unclear guidelines that don’t meet real-world needs. 

Technical leaders want vendors to go the extra mile, and that means offering simpler APIs, better documentation, and faster setup times. 

In short, they want tools that respect their time and scale with their needs. 



We asked 100+ CTOs how long it takes to implement search. You're not gonna like the answer

Algolia review: The enterprise giant meets the developer favorite – Meilisearch

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.

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