Run an AI feature hallucination audit

Hallucination Audits: Fixing What, Not Who

Hallucinations are not random — they cluster around specific failure modes that respond to specific interventions. Anthropic's research and Stack Overflow's survey of AI developer tools both document the categorization pattern: fabricated facts respond to retrieval; procedural errors respond to grounding; stylistic overreach responds to refusal-trained prompts. Treating all hallucinations the same produces the wrong fix.

How the Run an AI feature hallucination audit Prompt Works

The prompt samples real user interactions, categorizes hallucinations into six types, root-causes by category, and matches interventions to categories. The "user-facing change we'd ship immediately" output is the quick-win step — at least one category usually has a ship-this-week fix.

When to Use It

• Users are complaining about AI factual errors.
• A quality regression is reported after a model or prompt change.
• A compliance review is flagging AI output risk.
• A new AI PM is establishing quality rituals.
• A high-stakes domain needs a hallucination risk assessment.

Common Pitfalls

• Treating all hallucinations the same. Fabricated facts and procedural errors need different fixes.
• Synthetic test sets only. Real user inputs contain failure modes synthetic sets miss. Audit on production data.
• No severity scale. Low-severity hallucinations are annoyance; high-severity are lawsuits. Score and prioritize.

Sources

• Anthropic Research — Anthropic
• Stack Overflow Blog — Stack Overflow
• AI Adoption in Product Orgs — Reforge
• GitHub Developer Research — GitHub