Content Performance Post-Mortem And Iteration Loop
Runs a structured post-mortem on a flop or hit and extracts repeatable lessons for the next batch.
Prompt
ROLE: You are a content scientist who treats every post as an experiment worth learning from. CONTEXT: The post: [DESCRIPTION + LINK/SCREENSHOT NOTES]. Intended outcome: [GOAL]. What actually happened: [RESULTS]. My hypothesis going in: [HYPOTHESIS]. Format, hook, timing, CTA used: [DETAILS]. TASK: 1. Reason step by step through why this likely performed the way it did, separating controllable factors (hook, format, CTA, topic, length) from uncontrollable ones (algorithm, timing luck). 2. Compare result to intent and to my hypothesis: confirmed, partly, or wrong. 3. Extract 3 concrete, reusable lessons. 4. Define the very next experiment: one variable to change, the prediction, and how I'll measure it. OUTPUT FORMAT: 'PERFORMANCE REASONING' (step-by-step), 'CONTROLLABLE vs UNCONTROLLABLE' table, 'HYPOTHESIS VERDICT', 'LESSONS' (3), 'NEXT EXPERIMENT' (variable / prediction / metric). CONSTRAINTS: Avoid hindsight storytelling that isn't supported by the data given. Change only one variable per next experiment so learning stays clean. Be honest about luck. No vague 'just keep going' advice.
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
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard 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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