Use case 01
Use it when you want to begin experimentation work without writing the first draft from scratch.
Product AnalystExperimentationAdvancedSingle prompt
Experiment Readout Template AI Prompt
Write a complete experiment readout for this concluded A/B test. Experiment: {{experiment_name}} Hypothesis: {{hypothesis}} Results data: {{results}} Audience: product team and... Copy this prompt template, run it in your AI tool, and use related prompts to continue the workflow.
Prompt text
Write a complete experiment readout for this concluded A/B test.
Experiment: {{experiment_name}}
Hypothesis: {{hypothesis}}
Results data: {{results}}
Audience: product team and leadership
1. TL;DR (3 sentences):
- What was tested, what was the result, and what is the recommendation?
2. Background:
- Problem being solved
- Hypothesis and expected direction of change
- Primary metric and guardrail metrics
3. Setup:
- Variants: control and treatment description
- Traffic allocation and targeting
- Test duration and sample size achieved vs required
4. Results:
- Primary metric: control value, treatment value, absolute difference, % difference, p-value, 95% CI
- Secondary metrics: same format for each
- Guardrail metrics: did any degrade significantly?
- Segment breakdown: results by key segments (mobile/desktop, new/returning, plan type)
5. Interpretation:
- Is the result statistically significant? Practically significant?
- Are results consistent across segments or driven by one segment?
- Any unexpected findings worth investigating?
6. Decision:
- Ship / Do not ship / Iterate / Run follow-up test
- If shipping: rollout plan (% of traffic, timeline)
- If iterating: what specifically changes in the next version?
7. Learnings:
- What did this test teach us about user behavior?
- How does this inform future experiments or roadmap decisions?
Return: complete experiment readout document suitable for sharing with the product team.When to use this prompt
Use case 02
Use it when you want a more consistent structure for AI output across projects or datasets.
Use case 03
Use it when you want prompt-driven work to turn into a reusable notebook or repeatable workflow later.
Use case 04
Use it when you want a clear next step into adjacent prompts in Experimentation or the wider Product Analyst library.
What the AI should return
The AI should return a structured result that covers the main requested outputs, such as TL;DR (3 sentences):, What was tested, what was the result, and what is the recommendation?, Background:. The final answer should stay clear, actionable, and easy to review inside a experimentation workflow for product analyst work.
How to use this prompt
1
Open your data context
Load your dataset, notebook, or working environment so the AI can operate on the actual project context.
2
Copy the prompt text
Use the copy button above and paste the prompt into the AI assistant or prompt input area.
3
Review the output critically
Check whether the result matches your data, assumptions, and desired format before moving on.
4
Chain into the next prompt
Once you have the first result, continue deeper with related prompts in Experimentation.
Frequently asked questions
What does the Experiment Readout Template prompt do?+
It gives you a structured experimentation starting point for product analyst work and helps you move faster without starting from a blank page.
Who is this prompt for?+
It is designed for product analyst workflows and marked as advanced, so it works well as a guided starting point for that level of experience.
What type of prompt is this?+
Experiment Readout Template is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.
Can I use this outside MLJAR Studio?+
Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.
What should I open next?+
Natural next steps from here are Product Experiment Prioritization.