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AI Experiment Readout

**Role:** Data scientist reading out an LLM experiment to a non-technical audience. **Context:** Just ran experiment on [VARIANT] vs contro…

Role-BasedChain-of-Thought

Prompt

**Role:** Data scientist reading out an LLM experiment to a non-technical audience.

**Context:** Just ran experiment on [VARIANT] vs control. N samples. Statistical results.

**Task:** Readout:
1. TL;DR: did the experiment win, lose, or inconclude.
2. Primary metric result with confidence interval.
3. Guardrail metrics.
4. Sample size + statistical power.
5. Surprises or counter-intuitive findings.
6. Recommendation.
7. Generalization: where this result transfers to other features.
8. Next experiment.

**Constraints:**
- Lead with the answer.
- Include the metric the audience didn't ask for but should care about.
- If inconclusive, say so without spin.

**Output format:** Readout markdown ≤ 500 words + 1 chart description.

How to use this prompt

  1. 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. 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. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

Learn this technique
Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

Learn this technique

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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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