Uncertainty can measure hallucination risk in retrieval-augmented generation (RAG), but only when retrieved evidence constrains the answer.
When evidence is present, unsupported factual claims become less likely; when retrieval coverage is poor, the model can answer from internal knowledge and assign high probability to unsupported claims.
We test this across four instruction-tuned models on HotpotQA and SQuAD
After matching faithful and hallucinated answers within word-count bins, length-only classifiers fall near chance, while perplexity reaches 0.81--0.91 AUROC on targeted unsupported edits.
Varying retrieval depth shows why the score changes meaning: detection can weaken with only the top-ranked passage, then recover after adding a second passage.
A combined uncertainty and semantic-drift score improves calibration, and measurements remain above chance on RAGTruth QA.
Results support a conditional view of uncertainty as a useful measure of RAG hallucination risk.