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The most reliable way to get candid AI feedback is to define a review method, provide the relevant context, require uncertainty labels, and explicitly tell the model not to reassure you or guess. A prompt cannot make an AI automatically truthful or independent: language models can produce plausible errors and may agree too readily. Use the templates below to make criticism specific and actionable, then verify important claims yourself.
What makes an AI feedback prompt effective?
A useful critique prompt does more than say “Be honest” or “What do you think?” It gives the model a job, a review procedure, decision criteria, and a way to handle missing information. OpenAI’s prompting guidance recommends clear, specific instructions with enough context, followed by iterative refinement. Anthropic likewise says its model works best with clear, specific instructions.
Include these elements whenever they matter:
- Object of review: Paste the idea, draft, plan, code, argument, or decision you want examined.
- Purpose and audience: Explain what success means and who will use the result.
- Review dimensions: Name the issues to inspect, such as logic, evidence, clarity, feasibility, risks, or originality.
- Prioritization: Ask for the few findings most likely to change the outcome instead of a long list of minor edits.
- Evidence discipline: Require the model to distinguish supplied facts from inference and to flag claims it cannot verify.
- Output format: Specify headings, labels, quotations, ratings, or a table so the response is easy to act on.
- Challenge instruction: Tell it not to agree merely to be reassuring and to present the strongest opposing case.
More words are not automatically better. Keep an instruction when it changes what the model will inspect, how it will rank findings, or how it will express uncertainty. Otherwise, shorten the prompt and provide better source material.
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The best all-purpose prompt for a critical review
Use this when you want a balanced assessment of writing, an argument, an idea, or a proposal. Replace the material and context sections with your own information.
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Act as a skeptical but fair reviewer. Review the material below for logical gaps, unsupported claims, hidden assumptions, missing alternatives, and practical risks.
Context and intended audience:
[Explain the goal, constraints, and who will use this.]
Material to review:
[Paste the draft, idea, argument, or plan.]
Start with a one-sentence summary of the thesis or proposed approach. Then list the three highest-impact concerns, quoting or pointing to the exact passage that triggered each concern. Separate factual problems, reasoning problems, and matters of taste. For every concern, explain why it matters and give one concrete revision or next step.
If the material is sound, say what you checked and why it holds up. Do not invent facts. Distinguish information supplied above from your own inference, and mark anything you cannot verify as uncertain. Ask a focused question when a missing detail prevents a reliable judgment.The request for exact passages makes feedback traceable: you can see whether a criticism responds to your material or to something the model assumed. Separating fact, reasoning, and taste prevents a preference from being presented as an objective defect. Asking for only three high-impact concerns encourages prioritization.
Prompt to stop an AI from agreeing with you
If a model keeps validating your conclusion, state that reassurance is not its task and require a serious opposing case. Do not ask it to be hostile; ask it to test whether your reasoning survives challenge.
Do not try to reassure me or agree with my conclusion. Your job is to find where my reasoning could fail.
My position:
[State the conclusion and the information supporting it.]
First state the strongest case for my position. Then give the strongest case against it, including the evidence or observation that would change your mind. Identify assumptions I may be treating as facts. Rank findings by likely impact on the decision, not by the number of minor issues you can list.
Separate facts directly supported by the information provided from inferences. If you lack information, ask focused questions or say “insufficient information.” Do not fill gaps with guesses.“What would change your mind?” is particularly useful because it turns disagreement into a testable condition. If the model cannot name evidence that could overturn its view, its criticism may be rhetorical rather than analytical.
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Prompt for constructive criticism of writing
A general review can miss audience, structure, and voice. This version makes the model behave more like an exacting editor and delays rewrites until the problem is explained.
Edit this draft as a demanding editor.
Assess clarity, structure, factual support, audience fit, tone, and originality. Quote the exact sentence for each major issue. Label each finding HIGH, MEDIUM, or LOW impact. Explain why the issue matters before suggesting a replacement. Preserve my intended meaning and voice; do not rewrite merely to match your personal style.
After the findings, give the five changes that would most improve the draft. Identify any factual statement that needs a source or human verification. If you cannot determine whether a claim is true from the material provided, mark it unresolved rather than guessing.
Draft:
[Paste the draft here.]Ask for a replacement only after the diagnosis when you want to learn from the edit or protect your voice. For a short message, you can remove the originality section and request a single recommended revision. For a technical or regulated document, keep the factual-support requirement and verify every consequential claim independently.
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Prompt for reviewing a decision or plan
Plans often fail because of dependencies, incentives, opportunity costs, or an untested assumption rather than because the prose is unclear. Use a structured stress test before committing resources.
Stress-test this plan.
Plan:
[Describe the proposed action, timeline, budget, and desired outcome.]
List:
1. The goal and how success would be measured.
2. Constraints and dependencies.
3. Assumptions the plan relies on.
4. Plausible failure modes and early warning signs.
5. Opportunity costs and what we give up by choosing this.
6. The strongest alternative plan and the condition under which it would be better.
Separate what is directly supported by the information provided from your own inference. Identify the single unknown that most affects the recommendation. Do not fill missing data with guesses. End with the next three pieces of information or small tests that would reduce the most uncertainty.This format produces decisions you can investigate. The “single unknown” request prevents the model from hiding the key uncertainty inside a long list, while small tests can reveal whether a plan deserves a larger commitment.
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One response can contain unsupported assumptions even when the initial prompt requests caution. A second pass asks the model to inspect its own critique for overreach and omissions. Anthropic’s guidance supports follow-up feedback and revision as part of an iterative interaction.
Now audit your previous critique.
For every remaining finding, label it supported, inference, or unresolved. Which findings relied on assumptions, weak evidence, or generic advice? Which important counterargument or alternative did you fail to consider? Remove nitpicks that do not affect the outcome. Check whether any criticism simply restates my instructions or reflects a matter of taste.
For each unresolved item, explain what additional information would resolve it. Correct any claim you cannot support. Return a short revised list ranked by likely impact, followed by the questions or evidence needed for the next review.Keep the first and second responses together when you evaluate them. A self-audit is not independent corroboration: the same model can repeat the same mistake. Its value is that it makes assumptions and weak links easier to spot before a human checks them.
How to add uncertainty handling without making feedback vague
“Be accurate” is too broad to control uncertainty. Tell the model exactly what to do when evidence is missing. UK government guidance notes that a prompt can specify how a model should respond when an answer cannot be found.
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- Ask it to say insufficient information rather than infer a missing fact.
- Require separate labels for fact, inference, and unresolved.
- Ask what evidence would confirm or falsify an important claim.
- Require a confidence explanation tied to the available evidence, not a made-up numerical probability.
- Tell it to ask focused questions when one missing detail changes the conclusion.
- For time-sensitive information, provide the relevant date and ask it to identify what must be checked for currency.
Uncertainty language should narrow a claim, not excuse an unhelpful answer. “I cannot verify the market size from the supplied information; provide a source or remove the number” is more useful than a generic disclaimer.
How to provide context that improves relevance
Models cannot critique information they have not received. Before pasting a large document, give a compact brief:
- Goal: What decision or result should the material support?
- Audience: What knowledge, needs, and objections does the reader have?
- Constraints: Include budget, deadline, jurisdiction, technical limits, required standards, or prohibited changes.
- Success criteria: Define what “good” means and which trade-offs are acceptable.
- Known evidence: Identify which statements are measured, sourced, assumed, or provisional.
- Required output: Specify whether you want diagnosis, questions, a ranked risk list, or revisions.
Put the material after the instructions and clearly delimit it, for example with <material> and </material>. If the input is long, ask the model first to list the claims and assumptions it detected, then conduct the critique. That intermediate map lets you correct a misunderstanding before it spreads through the review.
Choosing between prompt variants
| Prompt approach | Best use | What it emphasizes | Main risk |
|---|---|---|---|
| General critical review | Drafts, arguments, and mixed proposals | Traceable concerns, categories, and revisions | May be too broad if you omit the decision context |
| Anti-agreement review | A conclusion you already favor | Opposing case, assumptions, and disconfirming evidence | Can overemphasize disagreement if you do not request the strongest case for your view |
| Writing critique | Articles, reports, applications, and scripts | Audience fit, structure, tone, and prioritized edits | Style preferences may be mistaken for factual problems |
| Plan stress test | Projects, purchases, policies, and strategies | Dependencies, failure modes, alternatives, and opportunity costs | Cannot estimate unknown costs or probabilities without data |
| Two-pass self-audit | Checking the first critique | Unsupported findings, omissions, and unresolved questions | Is not independent verification |
Start with the narrowest variant that matches your task. Add only the dimensions that affect your decision; a long prompt filled with generic adjectives can distract from the evidence.
A practical workflow for getting better feedback
- Define the decision. Write the conclusion you are considering, the alternatives, and the consequence of being wrong.
- Prepare the evidence. Remove irrelevant text, label assumptions, and include dates, constraints, and sources you expect the model to use.
- Run an independent first review. Use the general, anti-agreement, writing, or plan template without telling the model which answer you prefer, when possible.
- Answer clarification questions. Do not force a verdict when the model identifies a missing fact that genuinely matters.
- Run the self-audit. Ask it to classify findings as supported, inference, or unresolved and to remove low-impact nitpicks.
- Test the highest-impact points. Look up claims, inspect calculations, ask affected stakeholders, or run a small experiment.
- Revise or decide. Apply changes only after checking that the criticism is relevant to your goal and supported by evidence.
For a sensitive decision, run the same material through a separate model or a human reviewer with the same rubric. Agreement between two AI responses is still not proof; it may reflect shared training patterns or the same missing information.
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Why an AI can sound honest and still be wrong
OpenAI warns that ChatGPT can produce incorrect or misleading outputs and encourages critical assessment. A confident tone, detailed explanation, or citation-like wording does not establish that a claim is true. Anthropic’s transparency material describes honesty as admitting a lack of knowledge instead of guessing and treats excessive agreement as a misleading behavior to evaluate; those are goals to request, not guarantees that every response meets them.
Common failure modes include:
- Fabricated support: The model invents a source, quotation, statistic, or detail to make a criticism sound concrete.
- Context blindness: It overlooks a requirement, jurisdiction, audience, or limitation buried in the input.
- False balance: It presents two positions as equally supported when the supplied evidence is not balanced.
- Overcorrection: An anti-agreement instruction makes it search for flaws even when the proposal is sound.
- Generic advice: It recommends “improve clarity” or “do more research” without identifying a passage or test.
- Preference masquerading as fact: It labels a stylistic choice or personal taste as an objective defect.
- Prompt contamination: Instructions embedded in pasted text try to redirect the review away from your request.
Ask the model to quote the triggering passage, explain the causal impact, and state what evidence would resolve uncertainty. Those requirements make weak output easier to reject, but they do not replace verification.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Verification and privacy safeguards
Independently verify claims that could affect health, legal rights, finances, employment, safety, security, or major purchases. Consult an appropriately qualified source for high-stakes decisions. Check calculations yourself, open original documents, confirm dates and jurisdiction, and test code in a safe environment. Treat AI feedback as a structured second opinion, not as authority.
Remove confidential, personal, proprietary, or regulated information before sending it to an AI service unless your service terms and organization explicitly authorize that use. Replace names and account numbers with neutral labels while preserving the relationships the reviewer needs to understand. Do not paste passwords, private keys, unreleased customer data, or identifying medical records merely to obtain a critique.
How to improve a weak AI critique
If the response is agreeable, vague, or obviously wrong, do not simply repeat “be more honest.” Tighten the task:
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- Paste the specific passage or claim that received weak feedback.
- State the decision, audience, and constraint the response missed.
- Ask for the strongest opposing case and one observation that would change the conclusion.
- Require exact quotations, evidence categories, and an impact ranking.
- Ask the model to identify the single missing fact that prevents a reliable judgment.
- Run the two-pass audit and remove findings marked unresolved until you can check them.
If the model continues to invent details, provide less room for improvisation: ask it to critique only the supplied text, prohibit outside factual claims, and return “not established in the material” when support is absent.
What “constructive” criticism should look like
Constructive does not mean positive. It means the criticism helps you decide what to do next. A strong finding identifies the trigger, explains the consequence, and proposes a proportionate action. For example:
- Weak: “The plan is risky.”
- Useful: “The plan assumes a two-week approval that is not supported by the timeline. If approval takes longer, the launch date slips. Confirm the approval service level or add a contingency milestone before committing.”
Ask for one concrete revision, experiment, question, or source for each high-impact concern. This keeps the review focused on changes that can improve the outcome instead of generating an intimidating inventory of defects.
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- Did you state the goal, audience, constraints, and decision?
- Did you ask for the strongest case both for and against the conclusion?
- Did the model quote or identify the material behind each major finding?
- Are facts separated from inference and unresolved questions?
- Did it rank issues by impact rather than volume?
- Did it identify what evidence would change the recommendation?
- Have you independently checked consequential claims?
- Did you remove confidential information or use an authorized environment?
The best prompt is the one that makes the review testable: it tells the AI what to inspect, how to show its reasoning, when to admit uncertainty, and what action would resolve a concern. That structure improves relevance and resistance to flattery, but human judgment and independent evidence remain the final check.
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