@ava-morgangrowth-marketing-evaluation-redteamテキスト公開2026/06/14 更新

Growth Marketing prompt that builds an evaluation suite for high-risk AI workflows and returns eval matrix, adversarial cases, grading rubric, and release threshold.

AI Workflow Experiments Growth Lifecycle Marketing

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Prompt

プレビュー

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# Role
You are a growth strategist who balances experimentation, positioning, and customer psychology. 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
Build an evaluation suite for high-risk AI workflows for AI-assisted acquisition, lifecycle messaging, creative testing, and funnel diagnosis.

# Inputs
- Project context: a self-serve AI analytics product entering a new vertical
- Target audience: growth marketers, lifecycle teams, founders, demand-gen leads
- Success metric: activation, quality, revenue, risk reduction, or cycle time
- Available tools and data: CRM, ad manager, web analytics, email platform, heatmaps
- Constraints: no dark patterns; clear hypothesis; one primary metric per experiment
- Desired depth: Fast triage
- Output tone: Clear operator memo

# Domain signals to inspect
- funnel metrics
- ad comments
- CRM stages
- landing page analytics

# Known risks to control
- vanity metrics
- brand mismatch
- overfitting to a single channel

# Method
1. Convert user outcomes into measurable tasks
2. Include realistic failure modes
3. Separate correctness from style
4. Define pass, warn, and block thresholds

# Required output sections
1. Success criteria
2. Golden tasks
3. Adversarial tasks
4. Rubric
5. Sampling plan
6. Release decision

# 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.

生成物

1 個の生成物

Example Output: Growth Marketing Evaluation and Red-Team Harness

Inputs used

Project context: a self-serve AI analytics product entering a new vertical
Target audience: growth marketers, lifecycle teams, founders, demand-gen leads
Success metric: activation, quality, and risk reduction
Available tools and data: CRM, ad manager, web analytics, email platform, heatmaps
Desired depth: Production-ready
Output tone: Clear operator memo

Generated Result

eval matrix, adversarial cases, grading rubric, and release threshold

Success criteria

Create at least 12 golden tasks: 6 normal cases, 3 edge cases, and 3 adversarial cases targeting vanity metrics. A passing result must cite the evidence source and state confidence.

Golden tasks

Create at least 12 golden tasks: 6 normal cases, 3 edge cases, and 3 adversarial cases targeting brand mismatch. A passing result must cite the evidence source and state confidence.

Adversarial tasks

Use CRM stages as evidence, apply the constraint "one primary metric per experiment", and explicitly note how the plan reduces overfitting to a single channel. The output should be ready for a practitioner to act on without a follow-up explanation.

Rubric

Use landing page analytics as evidence, apply the constraint "no dark patterns", and explicitly note how the plan reduces vanity metrics. The output should be ready for a practitioner to act on without a follow-up explanation.

Sampling plan

Release in three gates: internal dry run, limited pilot, then measured expansion. Each gate must show evidence that clear hypothesis is true in practice, not only in documentation.

Release decision

Release in three gates: internal dry run, limited pilot, then measured expansion. Each gate must show evidence that one primary metric per experiment is true in practice, not only in documentation.

Recommended Decision

Proceed with a narrow pilot focused on funnel metrics and ad comments. Treat vanity metrics as the primary launch blocker. The first milestone should prove that the workflow produces a usable experiment brief, campaign matrix, and measurement plan with clear evidence, named owners, and a review path for ambiguous cases.

Expected quality checks

The result is specific to AI-assisted acquisition, lifecycle messaging, creative testing, and funnel diagnosis.
It includes the required sections: Success criteria, Golden tasks, Adversarial tasks, Rubric, Sampling plan, Release decision.
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: vanity metrics.

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

Growth Marketing: Evaluation and Red-Team Harness

Use this prompt when you need eval matrix, adversarial cases, grading rubric, and release threshold for AI-assisted acquisition, lifecycle messaging, creative testing, and funnel diagnosis.

Best for

growth marketers, lifecycle teams, founders, demand-gen leads
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.