Turn a product idea into a coding-ready feature spec
Convert a rough feature idea into acceptance criteria, user stories, edge cases, and implementation notes.
Setup time
45 minutes
Time saved
3-5 hours
Best for
Solo founders, Product managers, Developers
Tools
ChatGPT, GitHub Copilot, Notion
Overview
This workflow helps builders clarify what to build before asking an AI coding assistant or developer to implement it.
When to use this workflow
Tools you need
ChatGPT
AI assistant
General AI assistant for drafting, reasoning, rewriting, and structured content generation.
Visit websiteGitHub Copilot
AI coding
AI coding assistant for code completion, explanations, and implementation support.
Visit websiteNotion
Workspace
Workspace for docs, databases, calendars, SOPs, and team knowledge bases.
Visit websiteStep-by-step workflow
Describe the outcome
Write what the feature should help the user accomplish, not just what UI should exist.
Tool used
Notion
Expected output
A plain-language feature goal.
Generate user stories
Ask AI for user stories, acceptance criteria, edge cases, and non-goals.
Tool used
ChatGPT
Expected output
A product-ready feature spec.
Add implementation context
Include stack, existing files, constraints, data shape, and testing expectations.
Tool used
ChatGPT
Expected output
A coding-ready prompt.
Send to coding assistant
Use the spec as the implementation brief and review the output against acceptance criteria.
Tool used
GitHub Copilot
Expected output
A clearer implementation cycle.
Prompt templates
Feature spec generator
Turn this rough feature idea into a feature spec. Include goal, users, user stories, acceptance criteria, edge cases, non-goals, data requirements, and test cases. Idea: [paste]Coding assistant brief
Rewrite this feature spec as an implementation prompt for an AI coding assistant. Include stack, constraints, files to inspect, acceptance criteria, and verification steps. Spec: [paste]Automation ideas
- Create specs from product board items
- Send accepted specs into GitHub issues
- Generate test cases when a spec is approved
Common mistakes
- Asking AI to code before clarifying behavior
- Skipping edge cases
- Treating generated code as done without testing
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