AI Guides › Workbench
By Nigel Guy · 6 min read
Most people read Claude's first answer, spot a mistake, and then ask "are you sure?" That feels like checking. It isn't: you have asked for a second opinion with no test to run against, so the model either defends the first answer or flips to a different one. Either way you have learned nothing about which was right.
The rule: a self-check only works when it names what the answer is being checked against. "Double-check this" is a mood; "check it against these criteria and the text I gave you" is a test.
Anthropic's own prompting guidance lists self-checking as a technique in its own right. It suggests appending something like "Before you finish, verify your answer against [test criteria]", and says this catches errors reliably, especially in coding and maths. Its separate guidance on reducing hallucinations adds three techniques that behave like self-checks: let the model say it doesn't know, make it pull word-for-word quotes before analysing a long document, and have it find a supporting quote for each claim, retracting any it can't support.
What that tends to catch in everyday work:
What it cannot do: catch a mistake the model doesn't know is a mistake. If a source is wrong, or the model's general knowledge is out of date, checking against itself repeats the error with more confidence. Anthropic's hallucination guide says plainly that these techniques reduce the problem and don't eliminate it, and that you should validate anything high-stakes yourself.
The guidance carries a model-specific exception. At the time of writing, Anthropic says Claude Opus 5 verifies its own work well without being asked, and that verification lines carried over from older prompts can cause over-verification, adding tokens and delay. For that model it advises removing them. The self-check advice stands for the other current models it lists, which includes Sonnet 5.5. Model line-ups change often, so check the current prompting guide before you bake a habit into a saved prompt.
Cost: this works on any Claude plan, including the free one, because it is just text in the message. Claude Pro was $20 a month on Anthropic's pricing page at time of writing (about £15 at time of writing, before tax; check the £ price at checkout), or $17 a month billed annually. You do not need a paid plan to use anything below.
| Check | What it does | Best for | The catch |
|---|---|---|---|
| Criteria check | Tests the answer against a list you supply | Drafts with firm requirements, maths, code | Only as good as your criteria |
| Quote-back check | Finds a supporting quote for every claim and removes those without one | Summaries and reports from documents you paste in | Needs the source text in the chat |
| "I don't know" permission | Lets the model flag gaps instead of filling them | Anything factual | Won't help if the model is confidently wrong |
| Fresh-eyes review | A second message asks for a critique, not a rewrite | Important writing | Can invent nitpicks; you must ask for evidence |
Add this to the end of any request where you can say what "right" looks like.
You are helping me with [TASK]. Produce the result, then check it before replying.
Requirements the result must meet:
1. [REQUIREMENT_1]
2. [REQUIREMENT_2]
3. [REQUIREMENT_3]
Before you answer, test your draft against each numbered requirement. Fix anything that fails. If a requirement is unclear or I haven't given you something you need, ask me instead of guessing.
Reply in this format:
- The final result.
- A "Checked" list: each requirement, then "met" or "fixed: [what changed]".
Keep the checked list short. Do not praise your own work.
Fill in: the task and two to four requirements you can verify by looking (for example "under 150 words", "every price in £", "no claim without a source I supplied").
Use when you have pasted a document and want a summary you can trust. It follows the pattern in Anthropic's hallucination guide: quotes first, claims second, retract anything unsupported.
You are a careful analyst. I have pasted a document below. Summarise it for [AUDIENCE] in [LENGTH].
<document>
[PASTE_DOCUMENT]
</document>
Steps:
1. Pull out the word-for-word quotes most relevant to [FOCUS]. Number them. If there are none, say "No relevant quotes found" and stop.
2. Write the summary using only those quotes.
3. Self-check: for every claim in your summary, name the quote number that supports it. Delete any claim you cannot support, and mark the gap with [removed].
4. Do not use outside knowledge. If the document doesn't say, say it doesn't say.
Output: the numbered quotes, the summary with quote numbers in brackets, and a one-line list of anything you removed.
Fill in: the audience, length, the focus, and the document. Read the quote numbers against the original; that spot-check is the point.
Short enough to keep as a standing line. It comes straight from the idea in Anthropic's guide that explicit permission to admit uncertainty reduces false information.
If you are unsure about any part of [TOPIC], or the information I gave you is not enough, say "I don't have enough information to be confident about [THE_PART]" rather than filling the gap. List what you would need to be sure.
Run this as a separate message after you have a draft you care about. Asking for a critique rather than a rewrite stops it quietly changing things you liked.
You are a sceptical reviewer who has not seen the brief before. Here is my brief and the draft.
<brief>[BRIEF]</brief>
<draft>[DRAFT]</draft>
Find up to [NUMBER] real problems: factual slips, anything in the brief the draft misses, internal contradictions, unclear sentences. For each, quote the exact line and say what is wrong. If you find fewer than [NUMBER], say so; do not pad the list. Do not rewrite the draft. End with the single change that would improve it most.
Fill in: the brief, the draft, and how many problems you will tolerate seeing (three is plenty).
For code, the better check is not a sentence at all. Anthropic's Claude Code guidance puts giving Claude a way to verify its own work (tests, a build, expected output) among the highest-value things you can do, because a failing test is evidence and a model's say-so is not.