DEV Community: Bristo Biju

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

Bristo Biju — Fri, 26 Jun 2026 22:52:40 +0000

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.

5 AI Prompts for Developers That Actually Work (Not the Generic Ones)

Bristo Biju — Mon, 15 Jun 2026 22:12:59 +0000

"Write me a function that does X."
Everyone starts here. And the results are fine — but fine isn't the reason developers are getting dramatically faster with AI.
The developers getting real leverage aren't using AI as a fancy autocomplete. They're using it as a thinking partner that happens to write code.
Here are 5 prompts that go beyond the obvious.

The Root Cause Debugger Most people paste broken code and ask AI to fix it. That often gets you a patch without an explanation — and you hit the same bug two weeks later. This code is producing unexpected behavior.

Expected output: [WHAT YOU EXPECTED]
Actual output: [WHAT ACTUALLY HAPPENED]
Environment: [YOUR LANGUAGE / RUNTIME / VERSIONS]

Walk me through your debugging process step by step.
Identify the root cause — not just the symptom.
Then provide a fix and explain why it works.

Code: [PASTE CODE]
The "step by step" instruction is what changes the output. You learn something every time instead of just copying a fix.

The Architecture Stress Tester Before you build something, this prompt finds the holes in your plan. I'm planning to build [DESCRIBE YOUR SYSTEM]. Expected scale: [USERS / REQUESTS PER DAY].

Act as a senior architect playing devil's advocate.
What are the top 5 ways this design could fail at scale?
For each failure, suggest a concrete mitigation.
I've caught two design mistakes with this prompt before writing a single line of code. Cheaper to fix in planning than in production.

The PR Description Writer Nobody likes writing PR descriptions. This makes it effortless. Write a pull request description for these changes: [DESCRIBE YOUR CHANGES].

Include:

What changed and why (not just what)
How to test it manually
Any breaking changes or migration steps
A checklist of things the reviewer should verify
Screenshots or GIFs placeholder if UI changed Your teammates will actually read your PRs now.

The Plain English Translator For when you need to explain something technical to a non-technical stakeholder without losing them in the first sentence. Explain this to a non-technical stakeholder who understands business but not code.

Rules:

Use one simple analogy
No technical jargon
Include the business impact in one sentence
Keep it under 150 words

Technical concept: [PASTE CODE OR CONCEPT]
Saved me from a painful 30-minute meeting last month.

The Prompt Refiner This one is meta — use it when your AI prompts aren't giving you good results. Here is a prompt I've been using: [YOUR PROMPT]

The results have been [TOO VAGUE / INCONSISTENT / MISSING X].

Rewrite this prompt to get better, more consistent results. Include:

Clear role assignment
Specific output format
Constraints and boundaries
A few-shot example if helpful AI improving your AI prompts sounds circular but it genuinely works. I've gotten 3x better outputs from prompts I've run through this.

The Full Toolkit
These 5 are from a larger pack of 40 prompts I use daily — covering code writing, testing, documentation, system design, DevOps, and AI engineering. All in a clean fill-in-the-blank PDF format.
Grab it here if you want the full set: [b4m.gumroad.com/l/wehfa]
Which of these 5 are you trying first? Let me know in the comments.

How I Generate Unit Tests in 30 Seconds With One ChatGPT Prompt

Bristo Biju — Tue, 26 May 2026 22:38:27 +0000

Writing unit tests is one of those tasks every developer knows they should do more of — and almost nobody enjoys doing.
It's repetitive, it's tedious, and it always feels like the least exciting part of shipping a feature. So I started using AI to do the boring parts for me.
Here's the prompt that generates a complete test suite in under a minute:
The Prompt
Write comprehensive unit tests for this function using [JEST / PYTEST / MOCHA — pick yours].

Cover all of these cases:

Happy path (expected inputs, expected outputs)
Edge cases (empty strings, zero values, max values)
Error conditions (what should throw or return an error)
Boundary values (min/max limits)
Null and undefined inputs

Add descriptive test names so each test reads like documentation.

Function: [PASTE YOUR FUNCTION HERE]
What You Get Back
Running this on a simple input validation function gave me 14 tests covering cases I would have never written manually — including a Unicode character edge case I genuinely hadn't thought about.
The test names alone are worth it. Instead of test('works correctly') you get test('returns false when email is missing @ symbol'). Your test suite becomes documentation.
The Secret Ingredient
The key is asking for named cases explicitly. When you say "write unit tests for this," AI writes 3-4 obvious tests and stops. When you list the specific types of tests you want, it's thorough.
Same principle applies to most AI prompts — specificity is the difference between mediocre and excellent output.
Pair It With This
After generating tests, run this follow-up prompt:
Look at these tests and identify any scenarios that are still missing.
Focus on: concurrency issues, integration points, and security-related inputs.

Tests: [PASTE GENERATED TESTS]
Nine times out of ten it finds at least one thing.
The Bigger Picture
I've built up a collection of 40 prompts like these — for code review, debugging, documentation, system design, DevOps, and AI engineering. If you want them all in one place: [b4m.gumroad.com/l/wehfa]
What's your least favorite part of writing tests? Happy path, mocks, or async testing? Let me know in the comments.

The AI Prompt That Replaced My Senior Engineer Code Reviews

Bristo Biju — Thu, 21 May 2026 22:25:38 +0000

I used to dread code reviews.
Not because of the feedback — because of the wait. You open a PR, ping your team, and sometimes sit idle for hours or days waiting for a review that takes 10 minutes to read.
Then I found a prompt that changed how I work.
The Prompt
Review this code as a senior engineer at a top tech company. Evaluate:

Correctness — does it do what it's supposed to?
Security vulnerabilities — SQL injection, XSS, exposed secrets, improper auth
Performance — bottlenecks, memory leaks, unnecessary loops
Readability — naming, structure, complexity
Test coverage gaps — what's untested?

Prioritize every issue you find as Critical / Medium / Low.

Code: [PASTE YOUR CODE HERE]
Why It Actually Works
Most people prompt AI with "review my code" and get a generic response. The difference here is two things:
Role assignment. Telling the AI it's a senior engineer at a top tech company consistently produces more thorough, production-minded feedback. It stops treating your code like a homework assignment and starts treating it like something that will run in production.
Priority tiers. Without them, AI gives you a wall of feedback with no sense of urgency. Critical/Medium/Low forces it to think about impact — and forces you to act on what actually matters instead of fixing a variable name when there's an auth vulnerability.
Real Example
I ran this on a Node.js API endpoint last week. It caught:

A missing input validation that could cause a crash (Critical)
An N+1 database query I'd completely missed (Medium)
Three variable names that were genuinely confusing (Low)

The whole review took 30 seconds. Not bad.
How To Use It

Copy the prompt above
Replace [PASTE YOUR CODE HERE] with your actual code
Paste into ChatGPT, Claude, or any LLM
Work through the Critical issues first, ignore Low until you have time

One More Thing
This is one of 40 prompts I've collected specifically for developers. If you want the full set — covering debugging, testing, documentation, system design, DevOps, and more — I packaged them into a clean PDF here: [b4m.gumroad.com/l/wehfa]
What do you use AI for most in your dev workflow? Drop it in the comments — always looking for what to cover next.

I Stopped Writing AI Prompts From Scratch. Here Are the 10 I Use Every Day.

Bristo Biju — Tue, 14 Apr 2026 22:37:06 +0000

Every developer I know is using AI now. But most are writing throwaway prompts — vague, inconsistent, and getting mediocre results.
I spent a few weeks refining the prompts I use daily into reusable templates. Here are 10 of the best ones. Just replace the [BRACKETED] parts with your specifics.

The Feature Builder
You are a senior software engineer. Write clean, production-ready [LANGUAGE] code for: [DESCRIPTION]. Include error handling, edge cases, and inline comments. Follow [FRAMEWORK] best practices.
Why it works: Assigning the "senior engineer" role consistently gets more thoughtful, production-quality output.
The Bug Detective
This code produces unexpected behavior: [PASTE CODE]. Expected: [EXPECTED]. Actual: [ACTUAL]. Walk me through your debugging process step by step, identify the root cause, and provide a fix.
Why it works: Forcing step-by-step reasoning catches issues that a direct "fix this" prompt misses.
Deep Code Reviewer
Review this code as a senior engineer at a top tech company: [PASTE CODE]. Evaluate: correctness, security, performance, readability, and test coverage. Prioritize issues as Critical / Medium / Low.
Why it works: The priority tiers mean you act on what matters instead of getting overwhelmed.
Unit Test Generator
Write comprehensive unit tests for this function using [TEST FRAMEWORK]: [PASTE CODE]. Cover: happy path, edge cases, error conditions, boundary values, and null inputs. Add descriptive test names.
Tech to Plain English
Explain this to a non-technical stakeholder: [CONCEPT/CODE]. Use a simple analogy, avoid jargon, and include a real-world business impact statement. Keep it under 150 words.
Why it works: Lifesaver before stakeholder demos.
PR Description Writer
Write a pull request description for: [DESCRIBE CHANGES]. Include: what changed and why, how to test it, breaking changes, and a reviewer checklist.
README Generator
Write a professional README.md for: [PROJECT DESCRIPTION]. Include: title, one-line description, features, tech stack, installation steps, usage examples, and contributing guide.
Prompt Refiner
Improve this AI prompt for better, more consistent results: [ORIGINAL PROMPT]. Rewrite it with clear role assignment, specific output format, constraints, and examples.
Why it works: Meta-prompting. Use AI to make your AI prompts better.
System Design Advisor
Design a scalable system for [USE CASE] handling [SCALE]. Cover: architecture, database choices, caching strategy, API design, and bottlenecks with solutions.
CI/CD Pipeline Builder
Write a [GITHUB ACTIONS/GITLAB CI] pipeline for a [LANGUAGE/FRAMEWORK] app. Include: linting, testing, security scanning, build, and deployment with rollback.

These 10 are from a larger pack of 40 I put together — if you want the full toolkit with all 7 categories, I packaged it as a PDF here: [https://b4m.gumroad.com/l/wehfa, https://payhip.com/b/lN2Aa, https://review-reply-ai.lemonsqueezy.com/checkout/buy/1256ba8e-6b3f-438f-8bf3-54958b30c1cb]
What prompts do you use most? Drop them in the comments — always looking to expand the collection.