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Why is my RAG answer wrong even though it retrieved the right document?
“Right document” can mean only that a result is topically relevant. It may not contain every fact needed to answer the question, and a retrieved chunk may combine useful evidence with irrelevant or misleading text. Even when the needed evidence is present, the model can overlook it, follow distracting material, or generate claims that none of the retrieved chunks support.
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These are different failure modes, not one generic retrieval problem. RAG has indexing and search stages, followed by prompting and inference. A successful search result does not verify what entered the final prompt or how the model used it. The RAG survey by Yu and colleagues explains why evaluating retrieval and generation separately cannot fully capture the performance of the whole system: Evaluation of Retrieval-Augmented Generation: A Survey.
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A result can be relevant to the topic while failing to support the specific answer. Likewise, a response can be factually plausible but unsupported by the supplied context, or supported by context but not answer the user’s actual question. Evaluate these dimensions separately: document relevance, answer correctness, faithfulness to evidence, and alignment with the query.
#1 Best Overall
Classify errors by their evidence
RAGChecker’s framework distinguishes errors based on the context behind the claim. An incorrect claim may be associated with relevant retrieved material, irrelevant retrieved material, or no retrieved material at all. It also measures context utilization: how much relevant information in the retrieved context the generator actually uses. These categories help distinguish misuse of evidence from distraction by noise and unsupported generation. See the RAGChecker paper.
How do I tell whether a RAG failure is retrieval or generation?
Work from the exact model input outward rather than relying on the retriever’s result screen. Then compare the answer with the evidence and with what the question required.
- Inspect the final assembled prompt. Record the exact chunks and any instructions that reached the model. A document shown in search results may have been omitted, truncated, reordered, or transformed before generation.
- Break the response into claims. Compare each claim with a reference answer or other reliable ground truth. Mark claims as correct, incorrect, or missing. Precision highlights unsupported or wrong claims; recall highlights required claims the response omitted.
- Trace each generated claim to its evidence. Ask whether the claim is supported by a relevant chunk, tied only to irrelevant context, or supported by none of the retrieved chunks. This separates evidence-use problems from unsupported generation.
- Check whether the context covers the question. A relevant chunk is not necessarily complete enough to establish every claim needed. Assess both the retrieved evidence’s coverage and whether the model used the relevant information that was present.
- Check query fit and abstention. Determine whether the response answers the original question. If the evidence is insufficient, assess whether the system should have said so instead of guessing. The survey treats response relevance and rejection when evidence is inadequate as distinct evaluation dimensions: Yu et al.’s RAG evaluation survey.
- Choose an intervention only after locating the failure. If evidence is missing or poor, investigate retrieval quality, alternate retrieval, filtering, or context recomposition. If the evidence is adequate but ignored or misused, focus on prompting, generation, and evaluation of evidence use.
What should you measure beyond retrieval relevance?
Retrieval relevance labels answer whether a result appears relevant to a query; they do not necessarily predict whether that result helps the downstream system produce a good answer. In their 2024 eRAG paper, Alireza Salemi and Hamed Zamani report that query-document relevance-label evaluation had a small correlation with downstream RAG performance. They propose evaluating retrieved documents through the downstream task instead. Their experiments report Kendall’s tau correlation improvements from 0.168 to 0.494 over baselines; these are study results, not a guarantee for another corpus or deployment. Read the eRAG paper.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteFor debugging, pair end-to-end answer evaluation with measures that expose where the pipeline failed. RAGChecker evaluates claim precision and recall, retriever coverage and precision, generator faithfulness, sensitivity to relevant and irrelevant noise, hallucination, and context utilization. The authors’ experiments used eight customized RAG systems; that figure describes their study setup, not a deployment recommendation. Read the RAGChecker paper.
Rank #3
- Query relevance: Is the retrieved material about the question?
- Evidence coverage: Does it contain the information needed for the answer?
- Claim support and faithfulness: Are generated claims justified by the supplied evidence?
- Answer precision and recall: Does the response avoid wrong claims and include the necessary ones?
- Context utilization: Did the model use relevant evidence that was available?
- Query fit and rejection: Did it answer the question, and did it decline when evidence was insufficient?
Whether to evaluate at document or chunk level, require human labels or reference answers, and include robustness, latency, and cost depends on the diagnostic question. Component tests isolate a stage; whole-system tests show whether the stages work together. Neither substitutes for the other.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When should you change retrieval or use corrective retrieval?
Change retrieval when the exact context supplied to generation is incomplete, low quality, or dominated by irrelevant material. Consider different retrieval, filtering, or recomposition, then verify that the new context covers the required claims. If the evidence was already adequate, changing retrieval alone may not address the generator’s failure to use it.
Rank #4
Corrective Retrieval-Augmented Generation (CRAG) proposes a retrieval evaluator that can guide selective retrieval actions when retrieved material is unreliable. It is one proposed approach, not evidence that the same intervention fits every production system. Read Yan and colleagues’ CRAG paper.
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