Why does my RAG chatbot give wrong answers? Usually because the right evidence did not make it all the way through the pipeline—or because the model made a claim the evidence does not support. Retrieval-augmented generation (RAG) indexes external information, retrieves passages for a question, and supplies them to a language model; a failure at any stage can produce an incorrect answer. Having access to a document collection is not proof that the relevant passage was parsed, retrieved, or included in the model’s final context. Start by inspecting that context before tuning generation settings.
Where can a RAG answer go wrong?
Think of RAG as a sequence: source files are parsed and indexed; a retriever finds candidate passages; the system ranks and assembles context; then the model writes an answer. Errors can originate at each handoff. LlamaIndex’s RAG overview describes the indexing, retrieval, and generation stages.
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The key diagnostic question is not simply whether the system has a document. It is whether the exact evidence needed to answer this question exists in the processed source, appears in the retrieved candidates, survives ranking and context limits, and supports what the model says.
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Capture one incorrect exchange end to end. Keep the question, retrieved candidates and their order (including scores if available), the final context passed to the model, the prompt, and the answer. Then trace the expected evidence forward through those artifacts.
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- Check the source. Does the original file contain the needed fact and its relevant conditions?
- Check parsing and indexing. Did the fact survive extraction and become available to the retriever?
- Check retrieval. Does the passage appear among the candidates returned for this question?
- Check context assembly. If retrieved, did it remain in the ordered context actually sent to the model?
- Check generation. If the evidence is in that context, does the answer stay within what it supports?
This sequence separates retrieval failures from answer-generation failures. Changing a prompt cannot restore a passage that was never retrieved; changing embeddings will not fix a model that ignores evidence already present in its context.
What should I check when the evidence is missing?
Confirm the corpus is complete and parsed correctly
Verify that the intended files were added and processed successfully. Inspect extracted text, especially tables, headings, footnotes, and sections where definitions or conditions appear. If the fact is absent or malformed in the indexed text, retrieval cannot return it. For systems using OpenAI’s vector-store file API, the file reference exposes processing status and errors, including unsupported or invalid files.
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Test chunk boundaries with real questions
A chunk can be too small to retain a fact’s definition or conditions, or so broad that the relevant material is diluted by unrelated text. Test alternative boundaries against representative questions rather than copying a setting from another system. LlamaIndex’s production RAG guidance also describes separating a retrieval-oriented representation from the larger context used for synthesis.
Chunking values are implementation settings, not universal accuracy rules. For example, OpenAI’s vector-store file reference documents an automatic chunking default of 800 tokens with 400 tokens of overlap. That is a platform-specific default, not evidence that those values suit every corpus or task.
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Look for exact-term and metadata misses
Inspect whether candidate passages contain exact names, identifiers, dates, or phrases from the question. Semantic embeddings can miss a passage that matches the query’s keywords; LlamaIndex discusses this limitation in its basic retrieval strategies. If failures cluster around exact terminology, compare keyword-sensitive or hybrid retrieval with semantic search. If questions depend on document type, date, or another attribute, check metadata and filters. Query rewriting or structured retrieval may help when the failure pattern points to those needs, but each option adds implementation complexity and should be evaluated on your own questions.
Search controls vary by platform. OpenAI’s Evals documentation describes file-search ranking configuration, including hybrid-search weights and a score threshold. Treat these as platform-specific controls, not settings available or appropriate in every RAG stack.
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What if the right passage is retrieved but the answer is still wrong?
Inspect ranking, truncation, and context selection
Do not stop at the vector database’s candidate list. Inspect the ordered passages actually passed to the model. The expected evidence may have ranked too low, been removed by a context limit, or lost necessary surrounding information during selection. Test reranking and context assembly, then verify that any concise context still contains everything needed for a complete answer. LlamaIndex’s production guidance covers structured retrieval and techniques that decouple retrieval chunks from synthesis context; its UpTrain integration guide describes checks including context reranking and conciseness.
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If the relevant evidence is present but the answer adds unsupported claims, review the answer prompt and generation behavior. Instruct the model to distinguish what the context states from inference, and to say when the supplied evidence is insufficient or ask for clarification. Prompt customization is among the approaches in LlamaIndex’s basic strategies guide.
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Evaluate the answer against the actual context supplied—not against the broader corpus the system could have searched. A fluent response is not proof of support. Faithfulness checks can identify claims that the context does not substantiate, while correctness against a reference answer is a separate question.
How do I know whether a fix worked?
Build a stable set of representative questions before changing the system. For each, record the expected source document or passage; where feasible, also keep a reference answer or explicit scoring guidance. Evaluate retrieval and response quality separately:
- Retrieval: Did the expected evidence appear, and how highly was it ranked? LlamaIndex documents retrieval measures such as hit rate and mean reciprocal rank in its evaluation guide.
- Response: Was the answer faithful to the supplied context, relevant to the question, and correct against the reference or scoring criteria? LlamaIndex’s evaluation guide covers response checks, and its answer relevancy API addresses query-response relevance.
Change one component at a time where practical, then rerun the same questions and compare both retrieval and response results. A retrieval change may improve evidence discovery while changing answer behavior in either direction, so measure both. There is no universally established benchmark size or pass threshold for every RAG application; set thresholds according to the task’s risk and users.
Which retrieval approach should I try?
Choose an approach based on the observed failure rather than assuming one method is best. Compare options using the same question set and assess keyword/entity matches, semantic paraphrase matches, the rank of the best evidence, answer support, context completeness after selection, and operating complexity. Structured retrieval and separating retrieval representations from synthesis context are documented techniques for production systems, not guaranteed fixes for every corpus.
Also consider data handling when evaluating hosted services. OpenAI’s data controls documentation describes retention behavior and controls; check the terms and settings of the specific service against your organization’s requirements.
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