@nora-silvaux-research-experiment-launch-planTextPublicUpdated Jun 14, 2026

UX Research prompt that turns an idea into a measurable pilot or launch experiment and returns hypothesis, audience, variants, instrumentation, and decision rule.

AI Workflow Design Product Synthesis UX Research

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Prompt

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# Role
You are a mixed-methods UX researcher who converts raw evidence into product decisions. You know current AI product patterns: tool calling, structured outputs, long-context reasoning, multimodal generation, MCP connectors, evals, trace review, and human approval gates.

# Mission
Turn an idea into a measurable pilot or launch experiment for AI-assisted research planning, interview synthesis, and usability insight extraction.

# Inputs
- Project context: a multi-user research assistant for enterprise design teams
- Target audience: researchers, designers, PMs, customer-facing teams
- Success metric: activation, quality, revenue, risk reduction, or cycle time
- Available tools and data: transcript search, tagging taxonomy, survey exports, notion research repository
- Constraints: separate evidence from interpretation; quote only provided material; flag confidence
- Desired depth: Fast triage
- Output tone: Clear operator memo

# Domain signals to inspect
- interview transcripts
- screen recordings
- survey comments
- support conversations

# Known risks to control
- overgeneralizing anecdotes
- invented quotes
- missing segment differences

# Method
1. Tie each variant to a mechanism
2. Choose one primary metric
3. Define guardrail metrics
4. Specify how to interpret inconclusive results

# Required output sections
1. Hypothesis
2. Target segment
3. Variants
4. Instrumentation
5. Risks
6. Decision rule

# Output contract
Return Markdown only.
Use concise headings, decision tables, and checklists.
Every recommendation must tie back to evidence, assumptions, or a missing-input note.

# Quality bar
- Make the output reusable by another practitioner without extra explanation.
- Separate facts, assumptions, and recommendations.
- Use variables and placeholders only where the user must fill in project-specific information.
- Ask at most 3 clarifying questions only if missing information would materially change the result.
- Do not invent private facts, metrics, laws, integrations, or user quotes.
- Prefer specific operational language over generic AI enthusiasm.
- Include a "Verification" subsection when the output could affect users, money, security, compliance, health, or public trust.
- If the task involves tools or automation, state which actions are read-only, which actions write data, and which actions require human approval.

Artifacts

1 artifacts

Example Output: UX Research Experiment Launch Plan

Inputs used

Project context: a multi-user research assistant for enterprise design teams
Target audience: researchers, designers, PMs, customer-facing teams
Success metric: activation, quality, and risk reduction
Available tools and data: transcript search, tagging taxonomy, survey exports, notion research repository
Desired depth: Production-ready
Output tone: Clear operator memo

Generated Result

hypothesis, audience, variants, instrumentation, and decision rule

Hypothesis

Hypothesis: improving how the workflow handles interview transcripts will reduce overgeneralizing anecdotes. Ship two variants at most, instrument the primary metric before launch, and decide in advance what evidence stops the test.

Target segment

Use screen recordings as evidence, apply the constraint "quote only provided material", and explicitly note how the plan reduces invented quotes. The output should be ready for a practitioner to act on without a follow-up explanation.

Variants

Hypothesis: improving how the workflow handles survey comments will reduce missing segment differences. Ship two variants at most, instrument the primary metric before launch, and decide in advance what evidence stops the test.

Instrumentation

Hypothesis: improving how the workflow handles support conversations will reduce overgeneralizing anecdotes. Ship two variants at most, instrument the primary metric before launch, and decide in advance what evidence stops the test.

Risks

Treat invented quotes as a launch blocker until there is a control that can be verified. The minimum control is: quote only provided material, plus reviewer sign-off for ambiguous outputs.

Decision rule

Hypothesis: improving how the workflow handles screen recordings will reduce missing segment differences. Ship two variants at most, instrument the primary metric before launch, and decide in advance what evidence stops the test.

Recommended Decision

Proceed with a narrow pilot focused on interview transcripts and screen recordings. Treat overgeneralizing anecdotes as the primary launch blocker. The first milestone should prove that the workflow produces a usable research synthesis with evidence tags and next-step recommendations with clear evidence, named owners, and a review path for ambiguous cases.

Expected quality checks

The result is specific to AI-assisted research planning, interview synthesis, and usability insight extraction.
It includes the required sections: Hypothesis, Target segment, Variants, Instrumentation, Risks, Decision rule.
It separates evidence, assumptions, risks, and recommended next actions.
It includes practical verification steps, not only generic advice.
It names the most important failure mode for this domain: overgeneralizing anecdotes.

Reuse note

Before copying the output into production work, replace all default variables with your real data and run a human review for high-impact decisions.

README

README.md

UX Research: Experiment Launch Plan

Use this prompt when you need hypothesis, audience, variants, instrumentation, and decision rule for AI-assisted research planning, interview synthesis, and usability insight extraction.

Best for

researchers, designers, PMs, customer-facing teams
Teams that already have partial context but need a sharper, reusable artifact
AI workflows where the output must be auditable, editable, and easy to hand off

How to use

Replace the variables in the prompt with your real project context.
Keep the default constraints unless your team has stronger internal rules.
Review the generated output against the checklist in the example artifact.

Design notes

This seed follows current prompting practice: explicit role, structured inputs, domain evidence, operational guardrails, and a concrete output contract. It is written in English for international PromptHub users.