I Used AI to Write My README, CI/CD Pipeline, and Docs — Here's Exactly How

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:

1. Lint — run [YOUR LINTER]
2. Test — run test suite, fail if coverage drops below 80%
3. Security scan — check for vulnerable dependencies
4. Build — create production build
5. Deploy to staging — on every push to main
6. Smoke test — verify staging is healthy
7. Deploy to production — manual approval required
8. 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.

Comments

[Marcus Kim]

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.