Retrieval-augmented generation (RAG) can produce a wrong answer even when the correct answer is in its reference data—or in the documents it retrieves. Retrieval only supplies context; the model still has to select relevant evidence, reconcile conflicts, combine facts, and keep its claims within what that evidence supports. Here, “ground-truth data” means reference answers or labeled evidence used to evaluate a system, not a special RAG data format.
Why ground truth does not guarantee a correct RAG answer
RAG has at least two distinct stages: a retriever selects documents, and a generator uses those documents to produce an answer. A correct reference answer can tell an evaluator what the system should have said, but it does not force either stage to behave correctly. Even when a relevant passage is available, the retriever may miss it, rank it poorly, or return it alongside misleading material. The generator may then overlook the useful passage, misread it, combine it incorrectly with other evidence, or add unsupported details.
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This is why “the answer was in the context” and “the answer was correct” are different claims. Evidence availability is not evidence use. The RGB benchmark evaluates noise robustness, rejection of unsupported questions, integration of information, and robustness to false information as separate capabilities; its authors report that evaluated models showed some noise robustness but struggled with negative rejection, integration, and false information handling. These findings describe the models and benchmark studied, not every RAG system. RGB, AAAI 2024
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Retrieval scores do not prove answer quality
A document judged relevant by a person may still fail to improve the final answer, while a document with a weak relevance label may contribute useful information in context. In the tasks examined by the eRAG authors, human provenance annotations had only a minor correlation with downstream RAG performance. They propose assessing retrieved documents by their effect on generation. That is a result from the study’s investigated tasks, not a universal relationship. eRAG, University of Massachusetts Amherst CIIR
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Clean test conditions can hide misleading-evidence failures
A benchmark built around gold documents or artificial perturbations may not capture how a system responds to plausible, misleading documents in a realistic retrieval set. RAGuard’s authors argue that idealized settings can overstate robustness and focus their benchmark on misleading retrievals. RAGuard, NeurIPS 2025 Datasets and Benchmarks Track
One answer-level score can conceal unsupported claims
An answer may resemble the reference overall while containing a false detail, reversing a negation, or omitting a required qualification. Similarity alone cannot establish that each claim is supported by retrieved evidence. RAGTruth provides a RAG-specific corpus for word-level hallucination analysis, allowing evaluation to examine unsupported spans rather than treating an entire answer as a single unit. RAGTruth, ACL 2024
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Failure modes to test directly
| Failure mode | What can go wrong | What to test |
|---|---|---|
| Retrieval noise | Useful passages are mixed with irrelevant or distracting ones. | Add irrelevant passages and vary their number and rank; check whether answer quality changes. |
| Failure to abstain | The evidence does not answer the question, but the model fills the gap confidently. | Include unanswerable questions and assess whether the system declines or clearly qualifies its answer. |
| Evidence integration | A multi-part answer requires facts from several documents, but the system uses only some of them. | Check both that all required facts were retrieved and that each appears correctly in the final answer. |
| Misleading or conflicting evidence | A plausible false passage or a conflict between sources steers the answer off course. | Introduce controlled conflicts and false material, then inspect whether the model follows it, flags the conflict, or corrects it. |
| Unsupported generated spans | The answer contains claims that the evidence does not support. | Label claims or spans for support instead of relying only on whole-answer similarity. |
| Operational instability | Behavior changes as retrieval depth, configuration, or corpus scale changes. | Repeat tests under the configurations and corpus sizes the system will actually use. |
Rejection and conflict handling deserve explicit attention, not just a general “accuracy” score. RGB treats negative rejection as a distinct ability, while ClashEval examines tension between a model’s internal prior knowledge and external evidence. ClashEval, NeurIPS 2024
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- Define what counts as ground truth. Use a reference answer where there is a meaningful expected answer, and label the evidence that supports or contradicts it. Record when multiple answers or qualifications are acceptable.
- Measure retrieval and generation separately. Inspect whether required evidence appears in the retrieved set, then score whether the final answer uses it accurately. Add an end-to-end measure so neither stage’s score is mistaken for overall performance.
- Build a varied case mix. Include answerable and unanswerable questions, noisy contexts, conflicting or misleading passages, and questions that require combining facts across documents. Report the mix so the score’s coverage is clear.
- Check claims against evidence. Review generated claims or spans for support, contradiction, omission, and unjustified certainty. Do not equate text similarity with correctness without checking negation and evidence alignment.
- Report errors by type. Separate missing evidence, irrelevant retrieval, incorrect rejection, integration errors, contradictions, and unsupported generation. Record the dataset, language, model, corpus, and version or date so results can be interpreted and reproduced.
- Repeat after meaningful system changes. Re-evaluate when the corpus, retrieval configuration, or retrieval depth changes; these can alter what evidence reaches the generator and how it responds.
The TREC RAG Track provides benchmark resources and frames the goal as answers that are relevant, accurate, updated, and contextually appropriate. Check the project page for the applicable resource year and version before comparing results. TREC RAG Track
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Stability and scale also matter: a system that performs well on one fixed setup may behave differently as its corpus or retrieval configuration changes. RAGGED explicitly studies stability and scalability as system-design dimensions. RAGGED, ICML 2025 / Proceedings of Machine Learning Research
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a benchmark score can—and cannot—tell you
A benchmark score summarizes performance on its selected questions, evidence, models, and scoring rules. It does not, by itself, establish that a deployed system will handle misleading evidence, abstain appropriately, integrate multiple documents, or remain stable after a corpus update. Compare evaluation methods on retrieval coverage, rejection of unsupported or misleading evidence, multi-document integration, claim-level faithfulness, conflict robustness, and stability under changes in retrieval depth and corpus scale. These are complementary dimensions; no single score or benchmark result establishes an overall winner.
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