Contextual Retrieval: Enhancing RAG Pipelines with Context-Aware Chunks

TL;DR

Q 1. What is Contextual Retrieval?

A method that annotates each document chunk with brief context so that when we embed or index it, we don’t lose key information. It involves “Contextual Embeddings” (embedding enriched chunks) and “Contextual BM25” (BM25 on enriched text). This fixes the common problem of context loss in RAG systems.

Q 2. Why is traditional RAG often insufficient?

Traditional RAG splits large documents into chunks (embedding and searching them) but strips away the original context. For example, the isolated chunk “The company’s revenue grew by 3%…” does not say which company or when. Without context, the system can retrieve the wrong information or answer inaccurately.

Q 3. How does Contextual Retrieval work?

It works in two phases. Phase 1: Preprocess the knowledge base by using an LLM to generate a concise context sentence for each chunk and prepend it (Contextual Embeddings) and include it in the BM25 index (Contextual BM25). Phase 2: Run the enhanced RAG pipeline on these enriched chunks. At query time, retrieve top-K chunks using both vector search and BM25 over the contextualized text, then optionally rerank the results.

Q 4. Can you give an example of Contextual Retrieval?

Imagine a financial corpus and the question “What was ACME Corp’s revenue growth in Q2 2023?” A vanilla RAG system might retrieve the snippet “The company’s revenue grew by 3% over the previous quarter”. Alone, that snippet doesn’t say which company or quarter. Contextual Retrieval would preprocess that chunk to something like: “This chunk is from an SEC filing on ACME Corp’s Q2 2023 results; the previous quarter’s revenue was $314M. The company’s revenue grew by 3%…”. The added context (“SEC filing on ACME Corp’s Q2 2023…”) disambiguates it so the right information is retrieved and used.

Q 5. What are the benefits and stats?

Experiments show Contextual Retrieval dramatically cuts retrieval failures. For example, simply using contextual embeddings dropped the top-20-chunk retrieval failure rate from 5.7% to 3.7% (a 35% relative reduction)anthropic.com. Combining contextual embeddings and contextual BM25 cut it to 2.9% (49% reduction)anthropic.com. Adding a reranking stage brought it down to 1.9% (67% total reduction)anthropic.com. These improvements mean more relevant information gets to the model, producing better answers with fewer “I don’t know” or wrong responses.

Q 6. What advanced techniques can augment Contextual Retrieval?

Contextual Retrieval itself can be combined with other RAG best practices. For instance, reranking (using an LLM or specialized model to rescore and filter top results) greatly boosts precision. Prompt caching helps reduce cost: caching the static parts of prompts (e.g. whole documents) can more than halve latency and cut costs by ~90%. Query expansion broadens search by adding related terms (an LLM can rewrite “climate change” to “global warming, environmental crisis”, etc.). These pieces work together in a modular pipeline to maximize relevance and efficiency.

Retrieval-Augmented Generation (RAG) is essential when an AI model needs domain knowledge that does not fit in its context window. In RAG, a large knowledge base is chunked, embedded, and searched so that relevant passages can be prepended to the user’s prompt. This way, the LLM generates answers grounded in actual data.

However, there’s a big catch: chunking removes context. Each snippet is treated independently of its original document or topic. As Anthropic notes, “traditional RAG solutions remove context when encoding information, which often results in the system failing to retrieve the relevant information”. In practice, this means an important piece of information (like the identity of a company or the timeframe of a fact) may be lost.

For example, consider asking “What was Dr. Smith’s primary research focus in 2021?” If the RAG pipeline returns a chunk that says “The research emphasized AI”, we still don’t know whose research or in which year. As Microsoft’s Azure AI team explains, without context names (“Dr. Smith”) or dates, even a relevant chunk can be useless.

The same issue appears in business or legal domains: a line like “net income doubled” means little unless you know which company and quarter it refers to. In short, traditional RAG pipelines can dilute meaning.

Contextual Retrieval was invented to fix the gap (document ingestion followed by user query → embedding → vector DB → prompt to LLM). In short, it reimagines the chunking process. Instead of letting chunks float alone, we glue a contextual hint onto them.

What Is Contextual Retrieval?

Contextual Retrieval is a new retrieval strategy for RAG pipelines. It augments each chunk of the knowledge base with brief, relevant context before indexing. This happens in two main ways (the “two sub-techniques” of contextual retrieval):

• Contextual Embeddings: Each chunk is prepended with a short sentence (or two) that explains where it came from. Then the whole chunk + context is fed to the embedding model. In effect, the vector embedding for a chunk now encodes both the chunk’s content and its situational context.
• Contextual BM25: Similarly, the same contextual sentence is included when building the BM25 index. So BM25 keyword search also “sees” the added context. In other words, we build the lexical index over the enriched text instead of the raw snippet.

These steps ensure that neither the semantic nor lexical search forgets the background.

In contrast, traditional information retrieval treats each chunk in isolation. For a vector search it only looks at the snippet’s content, and for BM25 only its raw words. No metadata or pointers to the original document are used.

Contextual Retrieval differs by modifying the data to be context-aware, rather than just changing the search algorithms. It’s like adding signposts to each page of a book: instead of finding a quote and wondering where it came from, you immediately know the book and chapter.

Traditional RAG Pipeline and the Context Loss Challenge

Let’s briefly recap how a standard RAG system is set up, to see where the context falls through the cracks. First, offline, you ingest your knowledge base:

1. Chunking: Split each source document into chunks (e.g. 200–500 token segments). This is necessary to fit in-memory or context-window limits.
2. Embedding: Use an embedding model (like OpenAI, Cohere, or Google’s embeddings) to convert each chunk to a vector.
3. Indexing: Store those vectors in a vector database (e.g. Pinecone, Milvus) for fast nearest-neighbor search. Optionally also index keywords with BM25 or TF-IDF for lexical search.

At runtime, when a user asks a question, the RAG system:

1. Query embedding: Embeds the user’s query (or reformulates it).
2. Semantic search: Finds the top-N chunks whose vectors are closest to the query vector.
3. BM25 search: (If hybrid) also finds top-K chunks by matching keywords.
4. Merge results: Combines the top chunks from both methods (deduplicating).
5. Prompt construction: Prepends those chunks to the LLM’s prompt.
6. Generation: The LLM (like GPT-4 or Claude) generates the answer using the retrieved context.

This flow is visualized in Figure 1 (above). It works well when chunks are self-contained, but in practice, chunking inherently can slice away important signals.

Example of context loss: Consider a document (e.g. an SEC filing) where chunk 17 contains the sentence “The company’s revenue grew by 3% over the previous quarter.” By itself, this tells us very little. Which company? Which quarter? A naive RAG system might retrieve this chunk for a query about ACME Corp, but because the chunk lacked identifiers, the system might not recognize it’s about ACME or Q2 2023. 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.

Another example from healthcare: “ER visits increased by 20%” means almost nothing without saying where and when. When chunks drop phrases like patient name, location, or date, the LLM can hallucinate or give irrelevant answers.

This context loss translates to RAG failures. Indeed, the baseline experiments failure rate (cases where the relevant answer chunk was not in the top-20 retrieved chunks) was about 5.7%. That means in ~6 out of 100 queries, RAG missed the needed info entirely. Contextual Retrieval cuts that failure drastically. But first, let’s see exactly how the two phases of Contextual Retrieval work.

The Two-Phase Approach of Contextual Retrieval

Phase 1: Preprocessing Chunks with Contextual Metadata

In Phase 1, we augment the knowledge base itself. Each chunk is paired with a short context sentence or two. In practice, a human or (more realistically) an LLM is asked to “explain this chunk’s place in the document.” For example, promt for an LLM can be like:

“<document> [full document text] </document>
Here is the chunk: <chunk> [chunk content] </chunk>.
Please give a short succinct context to situate this chunk within the overall document.”anthropic.com.

The LLM returns something like “This chunk is from an SEC filing on ACME Corp’s performance in Q2 2023; the previous quarter’s revenue was $314M.” That sentence is then prepended to the chunk text. The newly formed “contextual chunk” might read:

Original Chunk (extract)	Contextualized Chunk (after augmentation)
“The company’s revenue grew by 3% over the previous quarter.”	“This chunk is from an SEC filing on ACME Corp’s performance in Q2 2023; the previous quarter’s revenue was $314 million. The company’s revenue grew by 3% over the previous quarter.”

In the above table, the right column shows how adding context enriches the snippet. Now which company and quarter are explicitly included. This transformation is key. Once all chunks are contextualized, we then:

• Re-run embedding: Every chunk (with its new prefixed sentence) is embedded into a vector. This is the Contextual Embeddings part. Because the context sentence is included, the resulting embedding encodes the extra metadata. Queries about ACME or Q2 are more likely to match.
• Rebuild BM25 index: Similarly, we rebuild (or extend) the BM25 index on the enriched text. This is Contextual BM25. The context phrases become part of the keyword index.

By the end of Phase 1, our knowledge base is the same size but each chunk carries a clue. Importantly, this is done offline one-time. The quality of the context sentences depends on the LLM prompt and possibly domain knowledge.

Notes that even a generic prompt gives good results, but you can often improve by adding domain-specific hints or glossaries to the prompt. And about 50–100 tokens of context per chunk is enough to yield big gains.

※ Tip: The overhead of Phase 1 might seem large if you have millions of chunks, but it’s easily parallelizable and one-time. Using the prompt caching (storing the large document context and only querying the LLM for each chunk) can make this very cheap — on the order of $1 per million tokens of document text. With caching, you send the document once, then just the chunk, so you don’t pay repeatedly for the whole doc.

Phase 2: Enhanced Retrieval and Generation Pipeline

After preprocessing, the online pipeline uses standard RAG steps but on the contextualized data. The steps are:

1. Query embedding: The user’s query is embedded using the chosen model. Some systems even rewrite or expand the query first (more on that later).
2. Initial retrieval: Perform two searches: a dense semantic search on the contextual embeddings, and a lexical BM25 search on the contextual text. Both use the newly indexed chunks.
3. Merge & rerank: Combine the sets of retrieved chunks and optionally run a reranking model. The top 150 candidates, feed those (with the query) to a separate reranker. The reranker (often another LLM-based model) scores each chunk’s relevance, and pick the top K (they used K=20). This ensures only the best-contextualized info goes to the LLM.
4. Answer generation: The final top-K chunks (with context) are concatenated into the prompt along with the query, and the LLM generates the answer. Because each chunk carries its context, the LLM can more easily use it correctly.

This retrieval pipeline is essentially the same as before, except every piece of text now has built-in context. For example, if a chunk has “This is from an SEC filing on ACME Corp…”, even a generic query about Q2 revenue will find those chunks more reliably.

Notice that Contextual Retrieval is largely agnostic to the exact search tools used. You can use any embedding model, any vector store, any BM25 engine – the difference is simply that those tools operate on augmented data.

Example: Context Loss vs Contextual Enrichment

The best way to see the impact is through a concrete example. Suppose our knowledge base is a set of financial reports, and the question is: “What was the revenue growth for ACME Corp in Q2 2023?”.

• Standard RAG retrieval: It might find a chunk that has “The company’s revenue grew by 3% over the previous quarter.”. The LLM now sees that sentence and tries to answer. But it must infer which company and when. If “ACME Corp” wasn’t mentioned, the answer could come out incomplete or wrong. The system might even retrieve a similar sentence from a different company by mistake, since semantically “company revenue grew” matches many places.
• Contextual Retrieval enrichment: That chunk would be transformed ahead of time, e.g.: “This chunk is from an SEC filing on ACME Corp’s Q2 2023 performance; the previous quarter’s revenue was $314 million. The company’s revenue grew by 3% over the previous quarter.” Now if the user’s query mentions “ACME Corp Q2 2023”, it’s a much closer match (both semantically and lexically) to this enriched chunk. Even if the query is vague (“How did ACME do last quarter?”), the added context “ACME Corp’s Q2 2023” ensures the right snippet is found. The answer the LLM generates might then say, “ACME’s revenue grew 3% from the previous quarter”, and the model has the data to back it up.

We saw this above in the small table: the right column’s chunk literally contains “ACME Corp’s Q2 2023” as a phrase. That phrase matches the query terms, eliminating ambiguity. Effectively, we turned a pure semantic snippet into a nearly self-contained factoid by adding context.

Another way to illustrate context loss is the Dr. Smith example. If the retrieved snippet is “The research emphasized AI.”, it’s useless without knowing who did the research. Contextual retrieval might have appended “This is from an academic resume of Dr. Smith, year 2021” in front, making it clear to the model.

In summary, by enriching chunks, Contextual Retrieval gives the LLM both the “what” and the “who/when/where” of each piece of information. This is particularly important in specialized domains (medicine, law, finance) where specific identifiers matter.

Benefits and Performance Improvements

Empirically, Contextual Retrieval delivers impressive gains. The core metric is how often the relevant information is retrieved in the first place. Anthropic measured this by “top-20-chunk retrieval failure rate” – the percentage of queries where the correct chunk wasn’t in the top 20 results. Here’s what they found in average across multiple domains:

• Baseline RAG: ~5.7% failure rate. This is with the best standard system (embeddings + standard BM25 hybrid).
• + Contextual Embeddings (only): Reduced failure to 3.7% – a 35% relative drop.
• + Contextual Embeddings + Contextual BM25: Further reduced to 2.9% – a 49% drop versus baseline.

After these improvements, Anthropic added the reranking stage: they took the (larger) set of retrieved chunks and used a reranker to pick the final 20. This final step on top of contextual retrieval brought the failure rate down to 1.9% – a 67% reduction from the original 5.7%.

Standard embedding-only RAG fails ~5.7% of the time; adding BM25 brings it to 5.0%. Using Contextual Embeddings cuts failures to 3.7%, and combining with Contextual BM25 cuts it to 2.9%. (Reranking further reduces it to 1.9%.)

These numbers mean a lot fewer “missed” answers. In practice this translates to higher answer accuracy.

In short, Contextual Retrieval makes RAG more reliable. It solves the “long tail” of tricky queries that standard methods miss by addressing the context loss. The improvements (around 50% fewer retrieval errors) are significant enough to impact real-world applications – consider legal or medical QA where accuracy is critical.

Advanced Enhancements: Reranking, Prompt Caching, and Query Expansion

Contextual Retrieval is a powerful tool on its own, but it works best as part of a broader, optimized pipeline. Let’s look at some complementary techniques that teams often use:

• Reranking: After the initial retrieval, we often have dozens or hundreds of candidate chunks. A reranker is a second-stage model (often an LLM or specialized ranking model) that scores each candidate’s relevance to the query. In practice, you take the top-N (e.g. N=150) from embeddings+BM25, send them with the query to the reranker, and then pick the best K (e.g. K=20). This filtering ensures that only highly relevant context goes to the generative model. With reranking, the overall system becomes a three-stage process (retrieve → rerank → generate). Because Contextual Retrieval has already boosted recall, the reranker can focus on precision.
• Prompt Caching: When you are using an LLM repeatedly to preprocess chunks (or even during conversation), prompt caching can save a lot of time and money. The idea is to cache repeated parts of prompts (like the full document text or fixed instructions) so that the model doesn’t have to re-read them each time. For example, Anthropic’s Claude API supports prompt caching: you load a document once (costing maybe X tokens), and then for each chunk request you only pay for the chunk and the response. This can “reduce latency by >2x and costs by up to 90%” when doing contextual chunking. In practical terms, this makes Phase 1 far cheaper. You can analogously cache the prompt template used during querying (e.g. the instructions given to the LLM to answer), though that’s often less of a cost.
• Query Expansion / Rewriting: Sometimes it helps to enrich the user’s query itself before retrieval. Query expansion uses an LLM (or other model) to generate related query variants or add synonyms. For instance, if the user asks about “climate change”, the system might expand it to include “global warming, environmental impact” etc., then search for all of them. This can significantly improve recall, especially for sparse keyword matches. In a contextual RAG pipeline, query expansion can be an optional first step: it gives the retriever more angles to find the right chunks. It’s typically used for cases where users’ queries may be very terse or miss synonyms.
• Conversation Rewriting: In chat applications, a query rewriter might use the conversation history to clarify pronouns or ellipses. For example, if a user asks “What about their revenues?” after a conversation, the system needs to know “their = which entity?”. A small LLM can rewrite this to “What about ACME Corp’s revenues?” before retrieval.

These enhancements fit naturally with Contextual Retrieval. Prompt caching lowered the preprocessing cost to essentially nothing. The result was a RAG system that “dramatically improves retrieval accuracy” at low incremental cost.

Implementation Strategies and Best Practices

If you want to build a contextual retrieval system, here are some practical tips gleaned from the literature and experience:

• Chunk Sizing and Overlap: The way you split documents still matters. Typically, chunks of a few hundred tokens work well. Too small and you risk chopping sentences awkwardly; too large and you may dilute context. In general, tune the chunk size based on your content (legal text might chunk differently than conversations).
• Embedding Model Selection: The choice of embedding model can influence gains. Contextual Retrieval improves things across models, but some embeddings work even better. In your setup, try multiple embeddings (OpenAI’s text-embedding-ada, Cohere, etc.) and compare the retrieval metrics. The contextual approach is model-agnostic, so you can swap in any vectorizer.
• Prompt Engineering for Context Generation: The quality of the added context depends on the prompt you feed the LLM. For example, if your domain has special terminology, include a glossary or examples in the prompt. Or ask the model to format the context in a specific way (like “In one sentence, mention the document title and date” or so). The goal is to get a concise, factual context snippet. After generating contexts, quickly review a sample to ensure correctness (the LLM can hallucinate, so verify that the context sentence accurately reflects the chunk).
• Evaluation and Testing: Always benchmark your retrieval. Split off a test set of questions with known answer passages. Measure metrics like Recall@K or the “retrieval failure rate” (1 – recall@K). For example, track what percentage of test queries have the correct chunk in the top-20 results. Use these metrics to tune hyperparameters (chunk size, number of chunks, embedding choice). You can also evaluate end-to-end answer accuracy if possible, but raw retrieval metrics help pinpoint improvements.
• Combination with BM25: Don’t abandon lexical search. Contextual BM25 (which indexes the augmented text) often catches things that pure embedding search misses, especially proper nouns or numbers. As shown in Figure 1 (above), even standard RAG benefited from combining embeddings and BM25. After applying context, you should still run both. Then use a simple rank fusion (e.g. score normalizing or Borda count) to merge the hits. The “Contextual BM25” variant means your BM25 queries include the context words, so a query containing a specific company name will match chunks enriched with that company’s name.
• Number of Chunks to Retrieve: Another tuning knob is how many chunks to return. Contextual Retrieval often allows you to rely on fewer high-quality chunks. You could experiment with top-K = 5, 10, 20, etc. The tradeoff is that more chunks might include all relevant info but also introduce noise; fewer chunks is cheaper but might miss something. The reranking step helps here: it means you can safely retrieve a larger N, then trim to a smaller final set.
• Pipeline Modularity: Build your system modularly. One module for query rewriting/expansion, one for retrieval (embedding + BM25), one for reranking, one for generation. This way you can swap components. For instance, start with OpenAI embeddings and later try Gemini; swap Cohere reranker with a new one; adjust the prompt template, etc.

Following these strategies will help ensure your Contextual Retrieval system is both effective and practical.

Why Contextual Retrieval Matters for Next-Gen AI

As AI applications increasingly rely on large, domain-specific knowledge bases, Contextual Retrieval becomes essential. It’s one way to make LLMs truly know “what” and “where” information comes from. This is critical in many next-gen scenarios:

• Enterprise Assistants: Imagine an AI assistant for a company that has thousands of internal documents. When an employee asks about a project, the answer must come from the right internal docs. Contextual Retrieval helps ensure the AI knows exactly which product or quarter it’s citing.
• Legal and Healthcare AI: In law or medicine, facts without context can be dangerous. If an AI cites a case or study snippet, it must correctly identify the case or patient cohort. Adding context reduces misinterpretation.
• Personalized Chatbots: Chatbots that answer with personalized data (like user manuals, policies) need to anchor facts in the user’s context. Contextual chunks can include the user’s name or relevant policy section, making answers more trustworthy.
• Research & Scholarship: For tools that query research papers or technical docs, contextual retrieval prevents mismatched citations (e.g., quoting a paper’s result out of context).

In essence, Contextual Retrieval is a step towards “grounded” AI knowledge. Rather than treating text as a bag of words.

Moreover, the approach is timely. The growth of LLMs with larger context windows and better embeddings (Gemini, GPT-4o, etc.) means we can handle richer context injection than ever before. Coupled with techniques like prompt caching and multi-turn caching, the overhead of contextual enrichment is becoming minimal. Large AI vendors (Anthropic, Microsoft, open-source communities) are already adopting this technique because it pushes RAG to the next level of accuracy.

conclusion

Contextual Retrieval represents an evolution of RAG for modern needs. It addresses a fundamental weakness (loss of context) with a clever but straightforward idea (don’t lose context!) and shows measurable benefits.

For AI/ML engineers building LLM-based systems today, understanding and implementing contextual retrieval can make the difference between a so-so chatbot and one that reliably handles real-world knowledge. As knowledge bases grow and LLMs become even more central to applications, contextual retrieval will likely become a standard part of the toolkit — a bridge between human understanding and machine recall.