What to do when RAG retrieval is inaccurate: a troubleshooting and optimization guide for AI Agent knowledge bases
When RAG produces inaccurate answers, the easiest response is to switch models, but the problem often lies in the knowledge source, chunking, or query. You must first identify the stage at which the correct evidence was lost to avoid repeatedly tuning parameters without achieving any improvement.
First determine whether the problem lies in retrieval or generation
Break an answer into two stages: whether the retrieval results already contain the correct evidence, and whether the generated result faithfully uses that evidence. If the correct chunks were not retrieved at all, focus on checking the documents, chunking, and query. If the evidence is present but the answer is still incorrect, check context assembly, prompt boundaries, and output validation.
Layer one: check knowledge sources and metadata
- Delete outdated, duplicate, and conflicting versions, or clearly label their effective dates.
- Store metadata such as the source, version, department, and applicable products for each document.
- For tables, images, and scanned files, first confirm that their content has been extracted correctly.
- Permission filtering should take effect during retrieval and must not rely solely on hiding content during generation.
Layer two: adjust chunking instead of blindly pursuing a fixed length
Chunking should preserve complete semantic units whenever possible. Operating manuals can be divided by headings and steps, while policy documents can be divided by clauses. Chunks that are too small lose context, while chunks that are too large mix multiple topics. Overlapping windows can reduce the loss of information at boundaries, but they also increase duplicate results.
| Symptom | Possible cause | Priority action |
|---|---|---|
| The answer lacks prerequisites | Chunks are too small or split across sections | Re-chunk by heading or clause |
| Results mix multiple topics | Chunks are too large | Reduce chunk size and retain metadata |
| Duplicate citations | Too much overlap | Reduce overlap or deduplicate |
| Proper nouns cannot be found | Semantic retrieval only | Add keyword or hybrid retrieval |
Layer three: handle ambiguous user questions
“The data in last month’s report” does not specify the report, metric, or scope. The Agent can ask for clarification first, or perform query rewriting or decomposition when sufficient context is available. Complex questions should be divided into multiple verifiable subquestions before their answers are combined, rather than making a single retrieval operation handle all the reasoning.
Layer four: retrieval, reranking, and citations
Keyword retrieval is suitable for identifiers, product names, and specialized terms, while vector retrieval is suitable for questions that are phrased differently but have similar meanings. Hybrid retrieval can combine both types of candidates and then filter them through reranking. The final context should retain only the chunks directly relevant to the question and display sources in the answer to facilitate manual verification.
Continuously evaluate using failure cases
- Collect real questions and the relevant documents confirmed by human reviewers.
- Record the retrieval results and final answers separately.
- Classify each failure as a document, chunking, query, retrieval, ranking, or generation issue.
- Adjust only one major variable at a time and rerun the same test set.
- Add newly discovered failure cases to the regression set.
Frequently asked questions
How are RAG and a knowledge base related?
The knowledge base stores and organizes information, while RAG retrieves chunks based on a question and provides them to the generative model. Both information quality and retrieval strategy are essential.
Should you simply increase the number of retrieved results?
Not necessarily. More chunks may also introduce noise and conflicts. First determine whether the correct evidence has been retrieved, and then adjust the number of candidates and reranking.
How should you choose between RAG and fine-tuning?
Prioritize RAG when you need to update external knowledge and provide citations. Consider other methods when you need consistent behavior or output style. The two approaches solve different problems.
What to do next
In SmaugBrain’s knowledge base workflow, you can extract a set of real failed questions and record their causes according to the categories in this article. Change only one stage in each iteration and validate it using the same set of questions.