The useful result
Run a three-pass prompt that grounds answers in quotes and removes unsupported claims.
- Run a three-pass prompt that grounds answers in quotes and removes unsupported claims.
You paste a report and ask a question, and the answer includes a figure that is not in the report. Models can generate text that sounds right and is not, a failure usually called hallucination. It is worst when the model feels it must always give an answer.
Anthropic’s guidance lists three basic defences: allow the model to say “I don’t know”, ask for direct quotes before the analysis on long documents, and have it verify each claim with a supporting quote and retract any it cannot support. It also restricts the model to the documents you provide. None of this eliminates errors, but together they make mistakes much easier to see.
The examples below are illustrative exercises, not recorded results from any tool. Sources were checked on 24 October 2026; menus, limits and prices change, so confirm details on the linked pages.
Before you start
- A document or notes you want to ask about
- A question that has a definite answer in the text
- A few minutes to spot-check quotes
Review evidence
Documentation-based review · 2026-10-24
Primary documentation and official guidance checked on 24 October 2026. Examples, checks and exercises are original illustrations, not recorded tool tests. Cover artwork is a conceptual illustration.
Give permission to say it does not know
State plainly that the answer must come from the supplied text only and that “the document does not say” is an acceptable answer. Models that feel obliged to answer will fill gaps; permission to decline removes that pressure.
Check your result
Your prompt restricts the model to the supplied text and gives an explicit way to say the answer is not there.
Make it extract quotes before it answers
Ask for exact quotes from the document that are relevant to your question, numbered, before any analysis. Then ask for the answer using only those quotes and the quote numbers. If no quote supports a point, the point is left out.
Answer using ONLY the document below. Step 1: extract exact word-for-word quotes relevant to the question, numbered Q1, Q2… If there are none, write “No relevant quotes found.” Step 2: answer the question using only those quotes and cite them by number. If the document does not answer part of the question, say “The document does not say.”
Question: [question]
Document:
[paste]Example / expected result
Illustrative example; not a recorded model result.
“Q1: ‘Refunds are available within 14 days.’ Answer: Refunds are available within 14 days [Q1]. The document does not say how refunds are paid.”
Check your result
The answer cites quote numbers, and each quote can be found word for word in your document.
Verify each claim and remove what has no quote
Ask for a second pass: for each claim in the answer, find a supporting quote, and delete any claim that has none. Then search your document for three of the quotes to confirm they are genuine. For anything important, read the source yourself.
Review your answer. For each claim, give the supporting quote. If you cannot find one, remove the claim and mark where it was with [removed]. Then show the corrected answer.Example / expected result
Illustrative example; not a recorded model result.
A sentence about “fast shipping” is marked [removed] because the document never mentions delivery times.
Check your result
Every claim has a supporting quote you have verified, and unsupported claims are gone.
When it goes wrong
These techniques reduce errors but do not remove them. A quote can be real and still be misread, so check the context. Very long documents may need splitting. High-stakes decisions need a human expert.
Make it your own
Ask a question your document does not answer. Confirm the assistant says so, then ask a question it does answer and verify one quote in the source.
Useful next resources
Sources and review
Last checked Oct 24, 2026. Features and account limits can change. Verify important results against your original material.
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Practical AI lessons with checked sources, labelled examples and human verification.
