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A green trace shows that the instrumented steps completed; it does not show that retrieval found the right evidence, that the model used it faithfully, or that the answer addressed the question. To find the failure, inspect the exact request and retrieved evidence, compare that evidence with the context actually sent to the model, then check the answer claim by claim and evaluate its correctness and completeness separately.
Why is my RAG answer wrong even though retrieval succeeded?
“Retrieval succeeded” is usually a statement about execution, not evidence quality. A retriever can return documents without returning the passage that answers the question. The results can be relevant but incomplete, or the prompt assembly step can omit the useful passage before generation. Even when the model receives good context, it can make unsupported claims, repeat a source that is outdated, or answer only part of the question.
Databricks recommends logging inputs, outputs, and intermediate steps such as document retrieval so teams can diagnose whether a low-quality answer came from retrieval or generation. A trace that records only successful retrieval and generation leaves out the evidence needed to distinguish those causes. (Databricks, “Introduction to evaluation & monitoring RAG applications,” updated June 30, 2026.)
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How do I debug a RAG trace that looks successful?
Follow the evidence through the whole request, in order. Preserve the trace for the failing case so you can compare it with later runs.
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- Reconstruct the exact request. Record the original question and conversation history, any rewritten query, applied metadata filters, searched fields, and the retrieved document identifiers and text. Include retrieval scores and ranks, reranker results, the final assembled context, the prompt, model output, and citations. This shows whether the system searched for the intended question and what evidence it ultimately used.
- Check whether retrieval found enough evidence. Verify that the needed document exists in the indexed corpus, is current and parsed correctly, and was not excluded by a filter or search configuration. Read the returned chunks: do they contain the answer-bearing details, or only discuss the right subject? If a fact crosses a chunk boundary, inspect neighboring context. Evaluate relevance and coverage separately; a relevant result can still omit the decisive fact.
- Compare retrieval output with the assembled context. Check the exact text passed to the model, not just the retriever’s output. Prompt assembly can truncate, reorder, duplicate, or omit chunks. Irrelevant material can also compete with useful evidence. The RAGAS paper notes that context relevance matters and that useful information can be harder for a model to use when buried in a long passage.
- Audit the answer one claim at a time. Split the answer into claims that can be checked independently. For each claim, identify the exact supporting passage, or mark it unsupported, contradicted, or absent. RAGChecker describes claim extraction and checking for assessing faithfulness and hallucination. If the context is adequate but claims are unsupported, inspect the prompt instructions, model behavior, and output constraints.
- Check whether the answer resolved the question. Judge answer relevance and completeness separately from whether its statements are supported. A response may be grounded yet evasive, off-target, or missing one part of a multi-part question.
- Make the failure reproducible. Build a compact test set from real user questions and known source material. Include wording and complexity variations, as well as difficult cases involving missing evidence, conflicting versions, tables, long documents, exact dates or quantities, and questions that should receive an uncertainty statement or refusal. Keep the set fixed while changing one retrieval, chunking, prompt, model, or reranking variable at a time. Google Cloud’s December 19, 2024 guidance recommends representative questions, known-good outputs, repeatable metrics, and changing one variable between test runs.
How do I tell if it is retrieval or hallucination?
Use the relationship between context quality and answer support to narrow the cause. A claim is not a hallucination merely because it is wrong: it may be a faithful repetition of an incorrect or outdated source. Conversely, a statement can be true based on outside knowledge yet unsupported by the context supplied to the model.
| Dimension | Question it answers | Typical symptom when weak | Where to inspect |
|---|---|---|---|
| Context relevance | Are the retrieved passages about the question? | A grounded answer about the wrong subject | Query rewrite, filters, corpus, retrieval ranking |
| Context coverage or claim recall | Did retrieval include the evidence needed to answer? | An incomplete answer or a guessed missing detail | Corpus presence, chunking, recall, top-k, filters |
| Faithfulness | Does each answer claim follow from the supplied context? | Unsupported details or contradiction despite relevant passages | Assembled context, prompt, generator behavior |
| Correctness | Is the answer accurate against trusted ground truth? | A faithful repetition of an outdated or incorrect source | Source authority and version or date; reference answer |
| Answer relevance | Does the answer address the question asked? | True but irrelevant, evasive, or overly broad output | Query interpretation and response scope |
| Completeness | Does the answer resolve all parts of the question? | One part answered while another is omitted | Question decomposition, context coverage, answer structure |
| Citation precision and coverage | Do citations support the claims, and are needed claims cited? | Correct prose with misleading or missing citations | Claim-to-passage mapping and citation rendering |
A useful first diagnosis is the combination of context quality and support: poor relevance or coverage points toward retrieval or corpus problems; relevant context paired with unsupported claims points toward assembly or generation. High faithfulness alone does not prove correctness, because the source itself may be wrong. Salesforce’s diagnostic patterns likewise associate high faithfulness with low context relevance with retrieval trouble, and low faithfulness with high context relevance with generation or prompt trouble.
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Which metrics should I inspect?
Do not treat a single overall score as a verdict. Inspect each metric dimension that corresponds to the failure you are trying to locate, and calibrate acceptable thresholds to the application rather than assuming a universal pass mark.
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- Retrieve-and-generate evaluation: Bedrock lists correctness, completeness, faithfulness, citation precision, and citation coverage among its metrics. These describe distinct properties of the generated answer and its evidence.
- Claim-level evaluation: RAGChecker reports response precision and recall, retriever measures such as claim recall and context precision, and generator measures including context utilization, hallucination, and self-knowledge.
- Metric definitions: RAGAS defines faithfulness in terms of answer claims being inferable from context, answer relevance in terms of addressing the question, and context relevance in terms of keeping retrieved context focused.
These measures are diagnostic aids, not proof that an answer is correct. The cited documentation defines metrics and evaluation practices; it does not establish a universal accuracy guarantee for an automated judge. Review failures against the actual source text, especially when the answer turns on an exact claim, number, date, or conflicting evidence.
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What should a repeatable RAG test include?
Use the same representative queries and reference material to compare changes. A useful test set should reflect the ways real users phrase questions and the kinds of evidence your system must handle—not just clean, short questions with an obvious passage.
- Include multiple phrasings and levels of complexity for important user intents.
- Include cases with absent or conflicting evidence, tables, long documents, and claims that depend on exact dates or quantities.
- Include questions where the appropriate answer is to state uncertainty or refuse, rather than infer a missing fact.
- Keep known-good reference outputs and the source material stable across comparisons.
- Change one system variable at a time—such as retrieval settings, chunking, prompt, model, or reranking—so a result can be attributed to a change.
Google Cloud’s “Optimizing RAG retrieval: Test, tune, succeed” (December 19, 2024) recommends broad, representative test questions, known-good outputs, repeatable metrics, and one-variable comparisons. Use automated scores to identify patterns, then inspect individual failures to understand whether the system missed, misused, or misrepresented evidence.
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