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Just like in a regular conversation with a human, large language models (LLMs) also need to take context into consideration when providing answers.
In practice, LLMs operate with large datasets, often containing thousands or even millions of documents. However, only a small subset of that data is relevant to any given query.
Context distillation is the process of identifying and narrowing that information down before it's passed to the model.
In this article, we'll explain context distillation and how it pertains to your agentic AI workflows.
We will talk about:
- What context distillation is and why it's important
- How context distillation works
- Business problems context distillation can solve
- Limitations developers and business stakeholders might encounter
- Common context distillation techniques
- Real-world examples of context distillation
- The key differences between context distillation, model distillation, and LLM fine-tuning
- How Meilisearch can help with LLM context workflows
Let's get started!
What is context distillation in AI?
Context distillation is the process of selecting, filtering, and compressing input data so that only the most relevant information is included in an LLM's context window.
For example, say you have a textbook of information, but you only need to answer questions based on specific facts from it. Instead of reviewing the entire textbook, you pull only the specific passages needed to answer the question.
Context distillation applies this same idea to LLM inputs.
This typically happens as part of a pipeline (for example, retrieval-augmented generation), where external data is processed before being passed to the AI model.
Context distillation was first studied and reported in a research paper titled 'Learning by Distilling Context.'
Why is context distillation important for LLMs?
Context distillation is important for several reasons, mainly for cost, efficiency, and performance.
In an enterprise AI workflow, you can aggregate petabytes of data. Passing all available data into a model is both impractical and costly, especially given context window limits and token-based pricing.
In an enterprise agentic workflow, context distillation is important to:
- Reduce token usage (and associated costs)
- Improve latency by limiting input size
- Improve answers because of distilled data fed to LLMs
How does context distillation work?
Context distillation works by taking your large corpus of data and distilling it down to more relevant information to pass to the LLM.
You do this in a step-by-step process. Note that you might need several rounds of distillation to get more efficient answers.
The process of context distillation is as follows:
- Identify a subset of documents or data points related to the user's query (e.g., via search or embeddings).
- Narrow this subset further based on relevance, confidence, or business rules.
- Summarize, chunk, or restructure the data so it fits within the model's context window.
- Provide the distilled context as part of the generation prompt.
- Iterate retrieval and filtering strategies to improve output quality over time.
What problems does context distillation solve?
Context distillation reduces the amount of data passed to an LLM, helping address several common challenges in enterprise AI systems.
Enterprise AI agents work with (hundreds of) thousands of documents and databases spanning several petabytes. Using all this data harms performance and needlessly exhausts budgets.
A few problems developers solve with context distillation include:
- Making better use of limited context windows by passing only relevant data.
- Reducing prompt complexity, which improves the performance of agent responses.
- Lowering costs by using a smaller number of tokens.
- Reducing inconsistent or noisy outputs caused by irrelevant or conflicting context.
What are the limitations of context distillation?
Here are a few context distillation limitations to keep in mind:
- Initial setup and implementation can require significant time and resources.
- Over-optimizing filtering or compression rules can limit the range of possible responses and reduce output quality. Several rounds of testing might be necessary.
- Loss of flexibility when data is distilled too much.
- Difficulty handling dynamic or real-time data if the selected context is not frequently updated. This is especially limiting when you need to work with real-time data.
What are common context distillation techniques?
Developers have several distillation techniques available, and the one you use should align with your business problem.
We've compiled a list of techniques to help you decide on the best workflow for your business use case.
1. Prompt-based supervision
Prompt-based approaches rely on carefully structured prompts to guide the model toward using only relevant context. This typically involves appending instructions or constraints to the input.
This technique has a shorter learning curve for developers, but the output quality might suffer.
2. Synthetic dataset generation
Synthetic dataset generation uses AI-generated questions rather than relying on user-generated questions. These AI-generated questions can be used to identify and extract relevant context or test how well your system retrieves the right information.
This technique finds edge cases that a user might not have encountered yet, but developers have less control over input quality.
3. On-policy distillation
In on-policy approaches, the system uses its own generated outputs to refine how context is selected or structured in future iterations.
Using this model refines distillation, but it can also reinforce existing errors if not carefully monitored.
4. Iterative refinement
Iterative refinement uses several rounds of distillation until the quality of the input data meets expectations.
This technique outperforms other techniques, but it can take longer and cost more, depending on the number of iterations.
What are real examples of context distillation?
Here are a few examples of context distillation or closely related techniques that are used for research and enterprise solutions:
- Stanford Alpaca using Meta Llama: Researchers created an assistant using instruction tuning and synthetic data generation (often discussed alongside distillation techniques) to write essays, emails, and creative writing assignments.
- Anthropic's Constitutional AI for safeguards: Anthropic used structured prompting and alignment techniques to enforce guidelines (which can complement context distillation in practice) to build guidelines for its Claude LLM.
- Med-PaLM for industry-specific problems: Med-PaLM was fine-tuned on domain-specific medical data to answer complex medical questions and assist healthcare workers.
- Customer service models: Enterprises use context distillation to create customer service answers by retrieving and filtering brand-specific knowledge and product information.
When should you use context distillation?
The decision to use context distillation comes down to application, costs, time, business use case, and your current production environment.
Here are some guidelines for when you should use it:
- Repeated prompts: Reusing large or redundant context across requests wastes resources and your budget. Context distillation can lower this usage, saving on computing costs.
- Long instruction sets: When prompts include more information than necessary for a given query, performance may suffer. Use context distillation to narrow down data used in prompts and answers to improve performance and latency.
- Multi-step reasoning: Instead of repeating multi-step reasoning, you can reduce unnecessary context passed between steps to reduce latency and lower compute costs.
- Poor answer consistency: When you distill data, you can improve the consistency of answers in production.
How is context distillation different from model distillation?
The difference between context distillation and model distillation comes down to the way data is presented.
Model distillation reduces a model to a smaller one, but the goal is for the smaller model to still behave like the larger one.
Context distillation focuses on selecting and reducing the input context passed to an LLM during inference. It's especially useful in agentic environments where the same prompt is used repeatedly.
Here is a breakdown of the differences between context distillation and model distillation:
Is context distillation better than LLM fine-tuning?
Context distillation can use fine-tuning depending on the application, but fine-tuning is a separate approach that operates at the model level rather than the input level.
Context distillation reduces and filters the input context passed to the model to answer questions.
Fine-tuning is the repeated training of a model to improve its performance.
Here is a summary of the difference between the two:
How can Meilisearch support LLM context workflows?
Standard agentic LLM workflows work with static trained models. This is great for evergreen data that does not change much.
When you need more flexibility and to work with dynamic data, Meilisearch serves as a precision layer, without the costly development and training required for retrieval.
Meilisearch retrieves relevant chunks from your knowledge base to feed into an LLM, reducing tokens, costs, and latency while maintaining answer quality.
Your documents are ingested and converted to vector embeddings, which can later be used for your own search agents.
It can be applied across industries such as law, healthcare, technology, manufacturing, and retail.
Finally, Meilisearch helps reduce hallucinations. It queries your own documents, so queries asking for brand-specific information can still produce accurate answers.
Why context distillation matters for modern AI systems
Context distillation solves some of the important problems with agentic AI: accuracy, cost, performance, and token limits.
It's important to note that context distillation solves problems without losing your agents' ability to produce accurate answers to semantic queries.
It's perfect for specific industry knowledge or brand-specific queries. Eliminating repeated prompts makes your entire AI workflow more efficient.
How Meilisearch helps manage context before distillation
For context distillation, Meilisearch can ingest large enterprise-tier data silos to support downstream LLM workflows. It complements your retrieval and context pipeline.
