Contextual retrieval in AI systems \ Anthropic

For an AI model to be useful in specific contexts, it often needs access to background knowledge. For example, customer support chatbots need knowledge about the specific business they're being used for, and legal analyst bots need to know about a vast array of past cases.
Developers typically enhance an AI model's knowledge using Retrieval-Augmented Generation (RAG). RAG is a method that retrieves relevant information from a knowledge base and appends it to the user's prompt, significantly enhancing the model's response. The problem is that traditional RAG solutions remove context when encoding information, which often results in the system failing to retrieve the relevant information from the knowledge base.
In this post, we outline a method that dramatically improves the retrieval step in RAG. The method is called “Contextual Retrieval” and uses two sub-techniques: Contextual Embeddings and Contextual BM25. This method can reduce the number of failed retrievals by 49% and, when combined with reranking, by 67%. These represent significant improvements in retrieval accuracy, which directly translates to better performance in downstream tasks.
You can easily deploy your own Contextual Retrieval solution with Claude with our cookbook.
Sometimes the simplest solution is the best. If your knowledge base is smaller than 200,000 tokens (about 500 pages of material), you can just include the entire knowledge base in the prompt that you give the model, with no need for RAG or similar methods.
A few weeks ago, we released prompt caching for Claude, which makes this approach significantly faster and more cost-effective. Developers can now cache frequently used prompts between API calls, reducing latency by > 2x and costs by up to 90% (you can see how it works by reading our prompt caching cookbook).
However, as your knowledge base grows, you'll need a more scalable solution. That’s where Contextual Retrieval comes in.
For larger knowledge bases that don't fit within the context window, RAG is the typical solution. RAG works by preprocessing a knowledge base using the following steps:
At runtime, when a user inputs a query to the model, the vector database is used to find the most relevant chunks based on semantic similarity to the query. Then, the most relevant chunks are added to the prompt sent to the generative model.
While embedding models excel at capturing semantic relationships, they can miss crucial exact matches. Fortunately, there’s an older technique that can assist in these situations. BM25 (Best Matching 25) is a ranking function that uses lexical matching to find precise word or phrase matches. It's particularly effective for queries that include unique identifiers or technical terms.
BM25 works by building upon the TF-IDF (Term Frequency-Inverse Document Frequency) concept. TF-IDF measures how important a word is to a document in a collection. BM25 refines this by considering document length and applying a saturation function to term frequency, which helps prevent common words from dominating the results.
Here’s how BM25 can succeed where semantic embeddings fail: Suppose a user queries "Error code TS-999" in a technical support database. An embedding model might find content about error codes in general, but could miss the exact "TS-999" match. BM25 looks for this specific text string to identify the relevant documentation.
RAG solutions can more accurately retrieve the most applicable chunks by combining the embeddings and BM25 techniques using the following steps:
By leveraging both BM25 and embedding models, traditional RAG systems can provide more comprehensive and accurate results, balancing precise term matching with broader semantic understanding.
This approach allows you to cost-effectively scale to enormous knowledge bases, far beyond what could fit in a single prompt. But these traditional RAG systems have a significant limitation: they often destroy context.
In traditional RAG, documents are typically split into smaller chunks for efficient retrieval. While this approach works well for many applications, it can lead to problems when individual chunks lack sufficient context.
For example, imagine you had a collection of financial information (say, U.S. SEC filings) embedded in your knowledge base, and you received the following question: "What was the revenue growth for ACME Corp in Q2 2023?"
A relevant chunk might contain the text: "The company's revenue grew by 3% over the previous quarter." However, this chunk on its own doesn't specify which company it's referring to or the relevant time period, making it difficult to retrieve the right information or use the information effectively.
Contextual Retrieval solves this problem by prepending chunk-specific explanatory context to each chunk before embedding (“Contextual Embeddings”) and creating the BM25 index (“Contextual BM25”).
Let’s return to our SEC filings collection example. Here's an example of how a chunk might be transformed:
It is worth noting that other approaches to using context to improve retrieval have been proposed in the past. Other proposals include: adding generic document summaries to chunks (we experimented and saw very limited gains), hypothetical document embedding, and summary-based indexing (we evaluated and saw low performance). These methods differ from what is proposed in this post.
Of course, it would be far too much work to manually annotate the thousands or even millions of chunks in a knowledge base. To implement Contextual Retrieval, we turn to Claude. We’ve written a prompt that instructs the model to provide concise, chunk-specific context that explains the chunk using the context of the overall document. We used the following Claude 3 Haiku prompt to generate context for each chunk:
The resulting contextual text, usually 50-100 tokens, is prepended to the chunk before embedding it and before creating the BM25 index.
Here’s what the preprocessing flow looks like in practice:
If you’re i