RAG for AI Agents: Grounding Decisions in Real Data

Your AI agent is confidently wrong. It cites a policy that was updated six months ago. It references a product feature that does not exist. It provides pricing that changed last quarter.

This is not a hallucination problem. It is a knowledge problem. The agent's training data has a cutoff date. Everything after that date is invisible. Everything before that date might be outdated. And the agent has no way to know the difference.

RAG fixes this. Retrieval-Augmented Generation connects your agent to current, authoritative data sources so its responses are grounded in reality rather than frozen training data.

The RAG Pipeline: Three Stages That Make or Break Everything

RAG has three stages. Each one has traps that can undermine the entire system.

Stage 1: Indexing. Take your source documents, split them into chunks, generate vector embeddings for each chunk, and store them in a vector database. This happens offline, before any user queries.

Stage 2: Retrieval. When the agent needs information, take the query, generate an embedding, search the vector database for the most similar chunks, and return the top results.

Stage 3: Generation. Include the retrieved chunks in the agent's context alongside the original query. The agent generates its response using both the query and the retrieved information.

Simple in concept. The devil is in every detail.

Chunking: The Foundation Nobody Gets Right

Chunking is the process of splitting your documents into pieces that can be individually retrieved. Get this wrong and nothing downstream works, no matter how good your embeddings or retrieval are.

The naive approach splits documents at fixed character or token counts. Every 500 tokens, cut. This is fast and easy. It also produces garbage chunks that start mid-sentence, split tables in half, and separate context from the statements that depend on it.

Document-aware chunking respects the structure of your documents. Split at section boundaries, paragraph breaks, or semantic shifts. Keep headers with their content. Keep tables intact. Keep lists together. This produces chunks that are meaningful in isolation, which is critical because they will be retrieved in isolation.

Chunk size matters enormously. Small chunks (100-200 tokens) are precise but lose context. A chunk that says "the deadline is March 15th" is useless without knowing what deadline it refers to. Large chunks (800-1000 tokens) preserve context but dilute relevance. A 1000-token chunk about a company's return policy, shipping policy, and pricing policy will be retrieved for all three topics, even when only one is relevant.

The sweet spot for most use cases is 300-500 tokens with meaningful overlap between chunks. The overlap ensures that context at chunk boundaries is preserved in at least one chunk.

Add metadata to every chunk. Source document, section title, creation date, document type, and any other relevant attributes. This metadata powers filtered retrieval, which is dramatically more effective than pure semantic search.

Embedding Quality Determines Everything

The embedding model translates text into vectors that capture semantic meaning. Two pieces of text about the same topic should produce similar vectors. The quality of this translation determines whether retrieval finds the right information.

Model selection matters. General-purpose embedding models work acceptably for most content. Domain-specific embedding models work significantly better for specialized content like medical, legal, or technical documentation. If your documents use specialized terminology, evaluate domain-specific models.

Embedding the query differently than the document can improve results. Some models are trained for asymmetric retrieval, where queries are short questions and documents are longer passages. Using a model designed for this asymmetry produces better matches than symmetric models.

Test your embeddings empirically. Create a set of test queries with known relevant documents. Run the queries against your vector database and check whether the correct documents are retrieved. If precision and recall are low, your embedding model is the likely bottleneck. Try a different model before tweaking anything else.

Retrieval Strategies Beyond Basic Similarity

Vector similarity search is the baseline. It works reasonably well for straightforward queries. It falls apart for complex, ambiguous, or multi-faceted queries.

Hybrid retrieval combines vector search with keyword search. Vector search finds semantically similar content. Keyword search finds exact matches. Combine the results using reciprocal rank fusion. This consistently outperforms either approach alone because each covers the other's blind spots.

Filtered retrieval uses chunk metadata to narrow the search space before running similarity. If the user asks about return policy, filter to chunks from policy documents before searching. This eliminates irrelevant results that happen to be semantically similar.

Re-ranking applies a more expensive model to the initial retrieval results. Retrieve the top 20 candidates with a fast vector search, then re-rank them with a cross-encoder model that scores query-document relevance more accurately. Return the top 5 after re-ranking. This two-stage approach balances speed and accuracy.

Multi-query retrieval handles complex questions by decomposing them. "How does the return policy compare to last year?" becomes two queries: "current return policy" and "previous return policy." Run each independently and combine the results. This catches information that a single query would miss.

Context Window Management

You have retrieved relevant chunks. Now you need to fit them into the agent's context window alongside the system prompt, conversation history, and the user's query. Space is limited and expensive.

Relevance ordering matters. Put the most relevant chunks first. LLMs attend more strongly to information at the beginning and end of the context. Bury the most important information in the middle and it might be ignored.

Deduplication prevents waste. If multiple chunks contain similar information, include only the most relevant one. Redundant context wastes tokens without adding value.

Dynamic context allocation adjusts how many chunks you include based on the query. A simple factual question might need one or two chunks. A complex analytical question might need ten. Use the query complexity to determine the retrieval budget.

Include source attribution. For each piece of retrieved information, note its source. This allows the agent to cite its sources in its response, which builds user trust and enables verification.

Keeping RAG Current

RAG is only as good as its data. Stale data produces stale answers.

Implement continuous indexing. When source documents are updated, re-index the affected chunks. When new documents are added, index them immediately. The lag between a document update and the index reflecting it should be minutes, not days.

Version your index. When you change chunking strategies, embedding models, or retrieval parameters, you are effectively creating a new index. Track these changes. Compare retrieval quality before and after. Roll back if the new configuration performs worse.

Monitor retrieval quality in production. Sample user queries and the chunks retrieved for them. Are the chunks relevant? Are important chunks missing? Is the system retrieving outdated information? This ongoing monitoring catches degradation that automated metrics miss.

Handle document deletion. When a source document is deprecated, remove its chunks from the index. Otherwise, the agent might retrieve and cite information from a document that your organization no longer considers authoritative.

The RAG Quality Loop

RAG is not a set-and-forget system. It requires continuous improvement.

Track retrieval metrics. Precision: what percentage of retrieved chunks are actually relevant? Recall: what percentage of relevant chunks are actually retrieved? These metrics tell you whether your retrieval is working.

Track generation metrics with retrieval context. When the agent produces a wrong answer, was it because the retrieved context was wrong, or because the agent misused correct context? These are different problems with different solutions.

Build a feedback loop. When users flag incorrect responses, trace back to the retrieved chunks. Add the correct information to your knowledge base if it is missing. Improve your chunking if the right information exists but is not being retrieved. Improve your prompting if the right information is retrieved but not used correctly.

RAG done well gives your agent something no amount of training can provide: access to truth as it exists right now. That is the difference between an agent that users tolerate and one they trust.