Why Your RAG Agent Returns Irrelevant Results

A retrieval-augmented generation agent is only as good as the passages it retrieves. When those passages are off-topic, the agent answers from irrelevant context or hallucinates to fill the gap. Irrelevant retrieval usually traces back to a handful of fixable causes in the indexing and search pipeline: poor chunking, mismatched embeddings, weak queries, or untuned retrieval settings. Diagnosing which one is responsible is the path to fixing relevance.

Poor Chunking Destroys Relevance

How you split documents into chunks has an outsized effect on retrieval quality. Chunks that are too large dilute the relevant content with surrounding noise, so the embedding represents an average that matches nothing precisely. Chunks that are too small lose the context needed to be meaningful, and splitting in the middle of a sentence or idea fragments the information a query needs. Aim for chunks that capture a coherent unit of meaning, and consider overlapping them slightly so ideas spanning a boundary are not lost. Preserving structure like headings and keeping related content together substantially improves which passages surface, and revisiting chunking is often the highest-leverage fix.

Embedding Mismatch Hurts Matching

Retrieval relevance depends on the embedding model placing similar meanings close together in vector space. If your embedding model is poorly suited to your domain, it may fail to recognize that a query and a relevant passage are related, especially for specialized terminology or jargon. Confirm you are using a strong, appropriate embedding model and that you embed queries and documents with the same model so they share a vector space. For domain-specific content, an embedding model tuned to that domain often dramatically improves results. Also verify your similarity metric matches how the embeddings were trained, since a mismatch quietly degrades every search.

Query Phrasing Misses the Target

The query you send to retrieval may not match how relevant passages are written. A user's terse or conversational question can use different vocabulary than the source documents, causing the search to miss good matches. Improve this by rewriting or expanding the query before retrieval, having the agent rephrase it into a form closer to the documents or generating several variations and merging results. Hybrid search that combines semantic similarity with keyword matching catches cases where exact terms matter, such as product names or codes, which pure vector search can overlook. Better queries frequently fix relevance without touching the index at all.

Retrieval Settings Need Tuning

Even a healthy index returns poor results if the search parameters are wrong. Retrieving too few passages can miss the relevant one, while retrieving too many floods the context with noise that distracts the model. Tune how many results you return and consider a relevance threshold that filters out weak matches rather than always returning a fixed count. A reranking step that reorders initial candidates by relevance before passing them to the model is one of the most effective improvements, because it corrects the cases where the right passage was retrieved but buried beneath less relevant ones.

Measure Relevance Deliberately

You cannot improve what you do not measure. Build a set of representative queries with known correct passages and evaluate whether your pipeline retrieves them. This reveals whether the problem lies in chunking, embeddings, queries, or settings, and it tells you whether a change actually helped rather than just feeling better. Regular evaluation keeps relevance from silently degrading as your document set grows and changes over time.