How can you detect hallucinations in LLM outputs?

30-second answerSay your answer out loud first, then reveal.

It depends on what you can compare against. With source context (RAG or documents), check groundedness: split the answer into claims and verify each against the context with an NLI model or LLM judge. Without context, use consistency methods (sample several answers and check agreement, as in SelfCheckGPT), verify against tools or knowledge bases (search, databases), or use model uncertainty signals (low token probabilities). For structured outputs, validate values against source systems.

Method	Needs	How	Limits
Groundedness / faithfulness check	Source context	Claim extraction → entailment check per claim	Context itself may be wrong
Citation verification	Citations	Check cited passage supports the sentence	Model may cite loosely
Self-consistency	Multiple samples	Disagreement across samples suggests hallucination	Costly; consistent errors pass
External verification	Tools / KB	Look up facts, run calculations	Coverage of tools
Uncertainty signals	Logprobs	Low-confidence tokens on key facts	Not always calibrated or available
Field validation	Source systems	Check IDs, dates, amounts exist and match	Structured data only

Production pattern for RAG: generate → groundedness check → if unsupported claims are found: regenerate with stricter instructions, remove those claims, or abstain / escalate.

Measure it: "unsupported claim rate" on the eval set and sampled production traffic, as a key metric.

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How can you detect hallucinations in LLM outputs?

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