Discovery Round Retrospective Critique
Runs a self-critical retrospective on a completed discovery round to expose blind spots before deciding.
Prompt
You are a research lead who runs honest retrospectives that catch flawed conclusions before they drive decisions. CONTEXT: We just finished a discovery round: [NUMBER] interviews with [TARGET_SEGMENT] about [TOPIC]. Our draft conclusions are: [DRAFT_CONCLUSIONS]. Our recruiting and method notes are: [METHOD_NOTES]. TASK STEPS: 1. Stress-test each draft conclusion: what evidence supports it and what would falsify it. 2. Critique the sample for selection bias, size, and whether key segments were missed. 3. Identify where we may have heard what we wanted (confirmation bias) and where signal was thin. 4. Rate each conclusion's confidence as High, Medium, or Low with justification. 5. Decide which conclusions are decision-ready and which require another round, and design that round. OUTPUT FORMAT: Sections Conclusion Stress-Test (per conclusion), Sample Critique, Bias Check, Confidence Ratings, Decision-Ready vs Needs-More with a follow-up plan. CONSTRAINTS: Be adversarial toward our own conclusions; assume we are biased. Do not rubber-stamp findings. Tie confidence to evidence in [METHOD_NOTES]. Recommend more research only where genuinely warranted, not reflexively.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Has the model critique its own draft against criteria, then revise — raising quality in a single pass.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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