How to Detect Hallucinations in LLM Outputs

Detect hallucinations by checking material claims against trustworthy evidence and the task's expected answer. A second model can help flag unsupported claims, but its judgment is another fallible signal.

Check claims against evidence

1. Separate the response into factual claims that matter to the task.
2. Identify which accessible source supports each claim and whether that source is current.
3. Check that the citation supports the actual statement, not merely the surrounding topic.
4. Record unsupported, contradicted, and unverifiable claims separately.

Build a labeled sample

Use examples from the intended workflow. Include correct paraphrases, outdated documents, fabricated citations, and questions the system should decline. Have reviewers apply the same rubric and resolve disagreements. Report the number of reviewed answers and the unit being measured: a claim, answer, or completed task.

Measure detection errors

Review both flagged and unflagged outputs. Precision describes how often flags are correct; recall describes how many labeled failures the detector finds. A detector that flags everything may have high recall and be unusable for review. Estimate results on a separate sample from the one used to tune the detector.

Choose an action by impact

An unsupported shipping estimate might need a clarification; an unsupported clinical claim may require a restricted workflow and qualified review. Decide thresholds and escalation from the consequences of error. There is no universal safe hallucination percentage or verified industry average supplied by this guide.

RAG and consistency checks have limits

Retrieved content can be stale or wrong, and repeated model answers can agree on the same error. Track retrieval quality and source versions alongside detection results. Keep the original response, evidence references, detector version, and final review outcome in an appropriately protected record.

References and further reading

• DriftRail integration documentation

Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.