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Onboarding Funnel Diagnosis

Use Onboarding Funnel Diagnosis to turn real workflow notes into structured chat output for review, decisions, and next actions.

OnboardingActivationGrowth
Preview

Chat Prompt

Recommended model

Claude Sonnet 4.6

Output format

Structured chat output

Preview

Chat Prompt

chat thread

Users sign up, open image generation, then leave before choosing a model. We show 18 models and no default.

Likely cause: the first decision is too wide and appears risky. Evidence to collect: model dropdown opens, hover time, failed first-run events, and search terms. Copy fix: label one default as best for product visuals and one as best for edits. Product fix: preselect a safe default and hide advanced models behind comparison. One-week experiment: default to the highest-success image model and measure first job completion.

Output

Cause / Evidence / Copy fix / Product fix / Experiment

Preview for Onboarding Funnel Diagnosis, focused on input context, structured reply, and actionable next steps.

Full prompt

Onboarding Funnel Diagnosis

Onboarding Funnel Diagnosis chat prompt with structured analysis, risks, recommendations, and next actions.

Recommended model: Claude Sonnet 4.6Output format: Structured chat output
Full prompt
Chat Prompt
You are an activation analyst. Turn onboarding funnel notes into a diagnosis with the likely drop-off cause, evidence to collect, copy fixes, product fixes, and a one-week experiment.

Usage notes

Add real context, constraints, target reader, current evidence, and expected output depth before running; do not use it as a generic chat question.

Prompt FAQ

Before you use this prompt

Quick checks for inputs, model fit, and how to adapt the template without weakening the result.

What should I prepare before using Onboarding Funnel Diagnosis?

Prepare real input notes, the business goal, constraints, available evidence, and the exact structure you want back.

How should I judge the response quality?

Check whether the reply separates facts from assumptions and gives risks, tradeoffs, and actionable next steps instead of generic advice.

Thread preview

Users sign up, open image generation, then leave before choosing a model. We show 18 models and no default.
Likely cause: the first decision is too wide and appears risky. Evidence to collect: model dropdown opens, hover time, failed first-run events, and search terms. Copy fix: label one default as best for product visuals and one as best for edits. Product fix: preselect a safe default and hide advanced models behind comparison. One-week experiment: default to the highest-success image model and measure first job completion.

Output

Cause / Evidence / Copy fix / Product fix / Experiment

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