The simplest development flow with AI that works

A simple, yet powerful way to leverage AI in your day-to-day without getting into steep learning curve of tools and processes.

AI tools for development evolve at a ridiculous pace. Every week there’s a new workflow, a new “ultimate” method, and a dozen opinions about how you should use them. Some are brilliant but complex. Some require a steep learning curve. Some feel like learning another profession just to write code.

It’s nearly impossible to both work and track this wildfire of innovation at the same time.

So here’s a practical alternative: a simple flow that works with any AI coding tool—VS Code + Copilot, Cursor, Claude Code, Antigravity, whatever. It takes 10 minutes to understand, and if you follow it, your speed and clarity go up immediately. It is battle-tested across real tasks, real teams, and real deadlines.

This flow doesn’t require mastering a special method or memorizing prompts. It mirrors how modern software engineering is quietly changing: less typing, more thinking; less magic, more structure.

Let’s get to it.

1. Start with a brief

Before touching an AI tool, write a short brief. Don’t overthink it. You can do it yourself or ask product to draft it. The goal is clarity, not poetry.

Something like this:

# Brief Title

Short basic description of the brief. What it should do and what’s the goal.

## User flows
1. User should be able to ...
2. User should do ...
3. ...

## Technical details
1. Database schema should have additional fields
2. Changes in controller method
3. ...

Add any necessary screenshots. Save it as brief.md.

This document becomes the anchor for the entire session. Without it, you and the AI drift.

2. Move to planning mode

Open your environment of choice—VS Code + Copilot, Cursor, Claude Code, anything—and give it the brief.

Now comes the critical step: planning mode.

Most AI tools have this mode. This is where the real work happens today.

Modern software engineering is increasingly about shaping correct abstractions, preparing schema changes, mapping flows across services, and making sure everything fits. AI will write the code, but you have to direct it correctly.

Switch your tool into planning mode and ask it to generate a plan for the change:

Here’s the brief @<link-to-brief.md> that we are going to work on. We are going to start with the backend. Analyze the brief, review the relevant files and suggest the implementation plan.

Review it carefully.

If your change spans multiple projects—frontend, backend, micro-services—split it into phases. Plan the backend first, review it, implement it. Then plan the frontend part and implement it. Divide and conquer keeps the AI focused and the output clean.

Once the plan feels solid, pause and ask the agent to review its own plan again. It often catches its own mistakes. That’s free quality assurance.

When everything makes sense, move forward.

3. Implement tests as a source of truth

Ask the tool to write tests before writing any actual implementation. This forces clarity and catches misunderstandings early.

Then stop.

Review the tests like you would review a junior developer’s work.

This rule matters: when AI writes your code and AI writes your tests, there is no ground truth. You must treat the tests as if you wrote them and your name is on the PR.

Once the tests look accurate and cover the right behavior, tell the AI to proceed with implementation.

4. Review the implementation like a real code review

AI will give you code quickly, sometimes brilliantly, sometimes recklessly. Go through it with the same attention you’d give a new intern’s pull request.

If something’s off, start another loop:

plan → implementation → review

Repeat as needed. It’s efficient and keeps everything aligned with your brief.

You’ll notice something interesting: the time you spend is no longer on typing code but on thinking through the system.

In other words, the part you were hired for.

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Conclusion

This flow is simple, but it’s powerful.

Your attention shifts to the parts of engineering that actually matter—problem definition, careful planning, architecture, correctness. Code writing becomes a commodity job handled by the machine, and it’s only getting better.

The key is to treat the plan and the tests as your source of truth. Everything else is implementation detail.

If you follow this loop, your development cycle improves dramatically—easily 4× faster, often more, and the output stays solid.

AI is not replacing developers. It’s replacing typing. The real work is still thinking. And this flow gives you the cleanest way to do exactly that.