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My RAG system was meant to stop making things up by answering from documents. Instead, it sometimes refused to answer at all. That apparent contradiction has a practical explanation: retrieved text does not guarantee either that the system has enough evidence or that the model will use it well. “Ghosting” is a useful way to describe an unhelpful silence, not a technical term.
Why does a RAG system refuse to answer?
Retrieval-augmented generation (RAG) adds retrieved material to a model’s prompt. It can give the model evidence to work from, but it does not guarantee that the passages are relevant, sufficient, or correctly interpreted. Google Research’s work on sufficient context treats two questions separately: did the system retrieve enough information to answer, and did the model produce an answer using that information? Google Research’s sufficient-context paper uses that distinction to examine answer and abstention behavior.
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A refusal can therefore have different causes. The retrieved passages may not contain the answer, or the model may decline even when the context is sufficient. The reverse failure matters too: a model may answer confidently when the evidence is inadequate. Refusing more often is not, by itself, proof of greater reliability.
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How can I tell whether retrieval failed or the model ignored the context?
Diagnose the stages in order. This sequence is a practical way to apply the cited evaluation work, not a universally benchmarked recipe.
- Inspect what retrieval returned. For the question that triggered a refusal or an unsupported answer, review the passages actually supplied to the model. Are they relevant to the question, and do they contain the facts needed to answer it?
- Decide whether the evidence is sufficient. A passage can be on topic but still omit a key detail. If the retrieved material does not establish the answer, a refusal may be appropriate; the retrieval result is the first place to investigate.
- Compare the answer with the supplied evidence. If the context does support an answer, check whether the model used it accurately or refused anyway. If the model made a claim the context does not support, treat that as an answer-grounding failure rather than assuming retrieval alone explains it.
- Check the answer-or-abstain decision. Ask separately whether the system answered when evidence was insufficient and whether it refused when evidence was sufficient. These are different errors and should not be collapsed into a single refusal count.
This makes “hallucination” a symptom to investigate rather than a root-cause diagnosis. The relevant question is not only whether the final response was wrong, but whether the evidence was available and how the model handled it.
How should I evaluate both hallucinations and unnecessary refusals?
Test representative questions that the documents can answer and questions they cannot. For each, inspect the retrieved context and judge the final response against it. This reveals whether a system is failing to retrieve evidence, failing to use evidence, answering without support, or abstaining unnecessarily.
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When comparing system changes, keep the dimensions distinct:
- Evidence retrieval and sufficiency: did the returned context contain what was needed?
- Correctness and support: was the response right, and was it supported by the context provided?
- Appropriate abstention: did the system decline when the evidence could not answer the question?
- Unnecessary refusal: did it decline when the evidence was sufficient?
- Study conditions: which task, model, and dataset produced the result?
These checks make trade-offs visible. A setting that reduces unsupported answers could also increase refusals on answerable questions; evaluate both outcomes rather than treating either one as the whole definition of reliability.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can selective generation reduce unsupported answers?
Google Research reports that its selective-generation method improved the fraction of correct answers among cases where the system responded by 2–10% for Gemini, GPT, and Gemma in the study’s tested settings. The denominator matters: this is a result about correctness among responses, not a promise that any RAG system will improve by that amount or answer more questions overall. Google Research’s overview describes the work and its scope.
The practical lesson is to compare any proposed remedy on both answer quality and refusal behavior, under conditions that match the intended task as closely as possible. The cited findings do not establish a universally best RAG architecture, retriever, prompt, or configuration.
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