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Question

How do I stop AI from making things up?

Verified Oct 17, 2026 Review by Jan 15, 2027 3 sources
Short answer

You can't stop it completely, but you can cut it sharply. Give the model the source material, tell it that 'I don't know' is acceptable, ask it to quote the passages it relied on, and check every number, name and citation before anything goes out. For repeated tasks, add an eval.

What it is

This question is about hallucination: confident but false output. It matters most in client-facing copy, reports and support answers.

How it works

These steps follow Anthropic’s published techniques, plus testing:

  1. Ground it. Paste or connect the documents the answer should come from (RAG).
  2. Allow abstaining. Tell the model it may say when the documents don’t contain the answer.
  3. Ask for evidence. For long documents, have it extract word-for-word quotes first and answer only from them. Ask for a quote or source for each claim, and tell it to retract any claim it can’t support. Then spot-check.
  4. Narrow the task. Smaller, specific questions fail less often than open-ended ones.
  5. Test. For recurring work, build a small eval with known answers.

Benefits

  • Fewer embarrassing errors in published work.
  • Faster review, because claims come with their sources.
  • A clear standard your team can follow.

Limits and risks

  • Grounding reduces errors but doesn’t eliminate them. Models can misread sources.
  • Fabricated citations can look real, so open them.
  • High-stakes topics (legal, medical, financial) need expert review regardless.
  • IonBin doesn’t offer a downloadable checklist. The steps above are the checklist.

Best-fit stacks

Stack 04

Marketing content & social

Research, write, design and publish on-brand content, with the agent handling the first draft of everything.

Agent compatibility

Not tool-specific. See Agent-ready for connector support by tool.

Levels come from each tool's recorded, sourced compatibility. How the levels work.

Sources

  1. Reduce hallucinations · Anthropicofficial accessed Oct 17, 2026
  2. What are AI hallucinations? · IBMreference accessed Oct 17, 2026
  3. Define success criteria and build evaluations · Anthropicofficial accessed Oct 17, 2026

Practitioner views are restated in our words and labeled as views, not measured results. Product facts come from official sources dated above.

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