Last month I shipped a side project in a weekend.
Not because I'm fast. Because I stopped doing the parts of development that don't require me to think — and handed them to AI.
The code itself? I wrote that. But the README, CI/CD pipeline, documentation, and test suite? AI handled 90% of it.
Here's the exact workflow.
Step 1: README in 3 Minutes
The moment a project is functional, I run this:
Write a professional README.md for this project.
Project: [ONE PARAGRAPH DESCRIPTION]
Tech stack: [LIST YOUR STACK]
Main features: [LIST 3-5 FEATURES]
Include:
- Title with emoji
- One-line description
- Features list with emojis
- Tech stack with badges (shields.io format)
- Installation steps (numbered)
- Usage examples with code blocks
- Environment variables table
- Contributing guide
- License section Output: a complete, professional README that would take me 45 minutes to write manually. Done in under a minute.
Step 2: CI/CD Pipeline
This used to take me an afternoon of documentation-reading and debugging. Now:
Write a GitHub Actions CI/CD pipeline for a [YOUR STACK] application.
Include these jobs in order:
- Lint — run [YOUR LINTER]
- Test — run test suite, fail if coverage drops below 80%
- Security scan — check for vulnerable dependencies
- Build — create production build
- Deploy to staging — on every push to main
- Smoke test — verify staging is healthy
- Deploy to production — manual approval required
- Rollback step — if production deploy fails
Add comments explaining each job.
Works with GitLab CI, Jenkins, or CircleCI too — just swap the platform name.
Step 3: API Documentation
Paste any route or controller and run:
Write API documentation for this endpoint in OpenAPI 3.0 format.
Include:
- Endpoint path and method
- Description of what it does
- All request parameters (path, query, body) with types and descriptions
- All possible response codes with example response bodies
- Authentication requirements
- Rate limiting notes if applicable
Code: [PASTE YOUR ROUTE/CONTROLLER]
Drop the output into Swagger UI and your API is documented.
Step 4: Architecture Decision Records
Every significant technical decision I make now has an ADR written in 2 minutes:
Write an Architecture Decision Record (ADR) for this decision:
Decision: [WHAT YOU DECIDED]
Context: [WHY YOU WERE MAKING THIS DECISION]
Options you considered: [LIST 2-3 OPTIONS]
Use standard ADR format:
- Title
- Status
- Context
- Decision
- Consequences
- Alternatives considered Future you — and your teammates — will thank present you.
The Time Saved
On my last project this workflow saved me roughly 6–8 hours of documentation and infrastructure work. Time I spent on actual product decisions instead.
All four prompts above are from a 40-prompt developer toolkit I put together — including prompts for code review, debugging, testing, system design, and AI engineering.
Full pack here: [b4m.gumroad.com/l/wehfa]
What part of development do you wish you could automate away? Drop it in the comments — might turn it into the next article.
Top comments (2)
The README-in-3-minutes part feels believable because the inputs are bounded: project description, stack, features, install steps, and env vars. I'd be a little more careful with the CI/CD output though: an 80% coverage gate, staging smoke test, manual production approval, and rollback path are exactly the places where generated YAML needs to be treated as a first draft and verified against how the team actually ships. The founder/engineer win is turning AI into a documentation clerk while keeping humans on the product and risk decisions.
This is the right lane for AI help: delegate the parts that are structured and reviewable, then keep ownership of the product decisions. README, CI, and docs can be great agent tasks if the human still verifies truth, commands, and edge cases. Generated documentation that lies is worse than no documentation.