The most reliable way to get useful criticism from an AI is to define a review method—not to ask, “Is this good?” Give the model your goal, audience, constraints and source material; tell it to separate facts from inferences, expose assumptions, rank problems by impact, quote the passage behind each finding and propose a concrete fix. Require it to say “insufficient information” rather than guess.
That wording can make feedback more focused and less flattering, but it cannot guarantee truth or independence. AI systems can produce confident, misleading claims, so verify consequential facts with authoritative sources and qualified professionals.
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What an honest-feedback prompt must contain
Vague requests produce vague reactions. A robust critique prompt gives the model six controls:
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- Enough context: State the intended audience, objective, constraints, deadline, success criteria and any relevant evidence. OpenAI and Anthropic guidance both emphasize clear, specific instructions and sufficient context.
- A repeatable method: Name the checks you want: assumptions, counterarguments, evidence gaps, risks, alternatives and revisions.
- Traceability: Require exact quotations, section names or line references so you can see what triggered a finding.
- Uncertainty rules: Tell the model to distinguish facts from inference, identify missing information and avoid invented details. UK government guidance specifically recommends telling a model how to respond when it cannot find an answer.
- Prioritization: Ask for the few issues most likely to change the outcome instead of a long list of cosmetic comments.
“Be honest” by itself is not a method. The instructions above turn honesty into observable behavior.
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The all-purpose critical-review prompt
Use this when you need a fair examination of an article, proposal, argument, product concept or email. Paste the material after the prompt and label any supporting documents.
Act as a skeptical but fair reviewer. Review the material below for logical gaps, unsupported claims, hidden assumptions, missing alternatives, and practical risks.
Start with a one-sentence summary of the thesis. 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.
If the material is sound, say what you checked and why it holds up. Do not invent facts; mark anything you cannot verify as uncertain. Distinguish information directly supported by the material from your own inference. Rank findings by likely impact, not by how many minor issues you can list.
Context:
- Goal:
- Intended audience:
- Constraints:
- Evidence or sources the review may use:
Material to review:
How to get better results from it
- Replace each context label with specific information. “For customers” is weaker than “For first-time managers at companies with 50–200 employees; the goal is a 10-minute presentation that wins approval for a pilot.”
- Attach source material or paste the relevant excerpts. If the model cannot inspect a source, say so and ask it not to infer what the source contains.
- Set a limit such as “three highest-impact concerns” when you need decisions rather than exhaustive copy editing.
- Ask for line references, headings or quoted phrases. This makes it possible to challenge a comment that is based on a misunderstanding.
- Run the review once, then inspect the evidence yourself before changing the work.
Prompt to stop automatic agreement
Use this when an AI keeps validating your conclusion or repeating your framing. It deliberately asks for the strongest opposing case and the evidence that would change the recommendation.
Do not try to reassure me or agree with my conclusion. Your job is to find where my reasoning could fail.
First state the strongest case for my position. Then give the strongest case against it, including evidence that would change your mind. Identify assumptions I may be treating as facts. Rank findings by likely impact, not by the number of minor issues you can list.
Separate what is directly supported by the information provided from your own inference. If you lack information, ask focused questions or say “insufficient information.” Do not fill missing data with guesses.
My conclusion:
Evidence and context:
This wording does not force the model to oppose you. If the evidence supports your view, it should explain why after testing the alternatives. The useful change is that agreement is no longer the default objective.
Prompt for a demanding writing critique
A writing review should distinguish clarity and structure from factual support and personal taste. Ask for explanations before rewrites so the model does not silently change your meaning.
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 every finding HIGH, MEDIUM, or LOW impact. Explain the problem before suggesting a replacement. Preserve my intended meaning and voice. Do not invent facts or citations; mark claims you cannot verify as uncertain.
End with the five changes that would most improve the draft. Separate factual problems, reasoning problems, style preferences and optional improvements.
Audience and purpose:
Required facts or sources:
Draft:
Useful options for different drafts
- Technical documentation: Add “check whether a reader can complete the task from the stated prerequisites and steps; flag ambiguous labels, missing error handling and version-dependent instructions.”
- Persuasive copy: Add “identify unsupported promises, unspoken objections and places where the call to action is unclear.”
- Academic or analytical writing: Add “check whether each conclusion follows from the evidence and flag correlation-causation leaps.”
- Personal statement: Add “preserve the writer’s voice and flag generic claims, unsupported superlatives and details that could be read as insensitive.”
Prompt to stress-test a decision or plan
Plans often fail through dependencies, opportunity costs or a single unknown that was never tested. This template makes those elements explicit.
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Stress-test this plan.
List the goal, constraints, assumptions, dependencies, failure modes, and opportunity costs you infer. Separate what is directly supported by the information provided from your own inference. Give the strongest alternative plan and the condition under which it would be better.
Identify the single unknown that most affects the recommendation. State what evidence or experiment would resolve it. Do not fill missing data with guesses. If the plan cannot be evaluated from the information supplied, say “insufficient information” and ask the smallest number of focused questions.
Plan:
Available evidence:
Constraints and non-negotiables:
When to ask for scenarios
If timing, budget or demand is uncertain, add: “Show best-case, base-case and worst-case outcomes. For each, state the assumption that drives it and an early signal that would tell me which case is developing.” Treat these as conditional scenarios, not forecasts.
Use a two-pass self-audit
A first critique can contain its own unsupported assumptions. Keep the original review in the conversation and use a second prompt to audit it.
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Now audit your previous critique. Which findings relied on assumptions, weak evidence, or generic advice? Which important counterargument did you fail to consider? Remove nitpicks that do not affect the outcome.
Mark each remaining finding as SUPPORTED, INFERENCE, or UNRESOLVED. For every INFERENCE or UNRESOLVED item, explain what additional information would resolve it. Check whether any recommendation conflicts with the stated goal, audience or constraints.The second pass is a quality-control step, not proof that the first answer is correct. Compare the audit with the original material and your own sources.
Build a prompt with six practical controls
1. Define the decision, not just the topic
“Review my marketing idea” leaves the success criterion unclear. “Tell me whether this launch plan can acquire 500 trial users in 30 days with a $5,000 budget” gives the model a testable objective.
2. Supply boundaries and audience
Include region, edition, time period, permitted evidence, legal or safety constraints and the reader’s expertise. A recommendation for a regulated industry cannot be judged by the same standard as a casual hobby project.
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3. Specify the evidence standard
Say whether the model may use only your supplied text, a named set of sources or general knowledge. Ask it to label every claim as directly supported, inferred or unresolved. This prevents a plausible-sounding assumption from being presented as a fact.
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Do not merely ask the model to “mention uncertainty.” Tell it what to do: ask a focused question, state “insufficient information,” list the missing variable or propose a low-cost test. Never ask it to complete a gap by guessing.
5. Require prioritization
“Find every flaw” encourages a long list of low-value nits. Request the top three issues by likely effect on the decision, then ask for minor issues separately only if you need a complete edit.
6. Define the output shape
Headings, severity labels, quotations, a rationale and one revision make feedback easier to act on. If you need a table, specify its columns; if you need questions, set a maximum number.
How to compare prompt variants
When you are deciding between two prompts, judge the behavior they elicit rather than their word count.
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| Criterion | Strong prompt signal | Weak prompt signal |
|---|---|---|
| Specificity | Names the task, audience, goal and constraints | Uses “thoughts?” or “is this good?” without context |
| Evidence traceability | Requires quotations, references or a supported/inference label | Requests a verdict with no basis |
| Uncertainty behavior | Allows “insufficient information” and asks for missing inputs | Demands a complete answer regardless of evidence |
| Prioritization | Ranks high-impact findings and limits the list | Produces many unranked observations |
| Actionability | Explains why an issue matters and gives a concrete revision or test | Labels something “weak” without a remedy |
| Resistance to flattery | Requests the strongest opposing case and disconfirming evidence | Asks only for encouragement or validation |
A longer prompt is worthwhile only when each instruction changes one of these behaviors. If it does not, shorten it and improve the context or source material instead.
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- Prepare the material. Remove irrelevant passages, identify confidential information and state the outcome you are trying to achieve.
- Set the review contract. Choose one template and fill in audience, constraints, evidence rules and output limits.
- Request an independent first review. Do not tell the model which criticism you expect; that can anchor its analysis.
- Inspect traceability. Check every quotation or reference against your text. A comment attached to the wrong passage is not useful evidence.
- Run the self-audit. Ask which findings are supported, inferred or unresolved and which alternatives were missed.
- Decide what to change. Accept a revision only when it improves the stated goal without violating a constraint. Ask a human subject-matter expert when the issue exceeds the model’s competence.
Fix common failure modes
The model agrees with everything
Replace “What do you think?” with the anti-agreement prompt. Require the strongest case against your position, assumptions treated as facts and evidence that would change the conclusion.
The feedback is generic
Add the audience, objective, constraints and the exact passages to inspect. Ask for quotations and one revision per concern. Generic advice often means the model had too little context to be specific.
The model invents support
Restrict the evidence it may use, prohibit invented facts and citations, and require an “insufficient information” response. Then verify each consequential claim independently.
The critique is a wall of minor edits
Set a maximum number of findings and rank by likely impact. Ask for cosmetic issues only after the high-impact review is complete.
The rewrite changes your intent
Request an explanation before any replacement and explicitly say “preserve my intended meaning and voice.” Compare the proposed text with the original rather than accepting it wholesale.
Different runs give different answers
Save the exact prompt, context and model settings, then compare the reasoning and evidence labels—not just the final verdict. Variation is a reason to investigate the underlying uncertainty, not to choose the answer you prefer.
Verification, privacy and high-stakes use
AI providers warn that language models can produce incorrect or misleading outputs. A confident tone is not evidence of accuracy, and a request for “honesty” cannot remove training-data gaps, misunderstood context or hidden assumptions.
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- Check dates, versions, calculations, quotations and primary documents yourself.
- Do not paste passwords, private keys, confidential business plans, personal identifiers or regulated data unless the service and your organization explicitly authorize that use.
- Use the model to identify questions, alternatives and tests; keep the final responsibility for consequential decisions with a qualified human.
Choosing the right template
| Your need | Start with | Most important addition |
|---|---|---|
| General argument or proposal | All-purpose critical review | Exact passages and evidence labels |
| You suspect confirmation bias | Anti-agreement prompt | Strongest opposing case and disconfirming evidence |
| Article, email or script | Demanding writing critique | Audience, voice and severity labels |
| Project, purchase or strategy | Decision/plan stress test | Dependencies, opportunity costs and the key unknown |
| You already received a critique | Two-pass self-audit | Supported, inference and unresolved classifications |
The best prompt is therefore the shortest one that supplies the needed context, defines a checkable method and gives the model a safe way to admit uncertainty. Use the templates as starting points, then adapt their fields to the decision in front of you.
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