AI-Assisted Coding Flows: A Developer's Guide to the Future

Let's cut through the noise about AI coding assistants. After months of integrating them into my workflow, I've found that they're not just another shiny tool—they're fundamentally changing how we write software. And if you're not adapting, you're falling behind.

Why Now? Because Time Doesn't Wait

The gap is widening. While some developers still debate the merits of AI assistance, others are shipping features in half the time. Yes, GitHub Copilot made AI mainstream, but tools like Cursor are pushing into territory I honestly didn't think was possible yet. The collaboration between human and AI is becoming less about autocomplete and more about genuine pair programming.

My Real-World AI Workflow

Through trial, error, and plenty of "wait, what just happened?" moments, I've developed an approach that actually works. No theoretical best practices—just practical steps that deliver results.

1. Set the Stage Right

Before writing a single line of code, I feed the AI what it needs to be useful:

• Project conventions (because every codebase has its quirks)
• Tech stack details (the real ones, not just the README)
• Documentation (both official and those crucial internal wiki pages)
• Library specifics (including the gotchas we've discovered)
• Team standards (the unwritten rules that make or break PRs)

This isn't just setup—it's the difference between an AI that helps and one that creates more work.

2. Break It Down

The key isn't to let AI loose on your entire codebase. Instead:

1. Get the Plan: Let AI propose an approach, but read it critically
2. Reality Check: Compare it against what you know about your system
3. Refine: Adjust based on those edge cases only you know about
4. Document: Track changes to the plan—they'll teach you what the AI misses

3. Build in Phases

For each piece:

• Write the code, but don't commit yet
• Test it (because AI is confident, not always correct)
• Update your plan when reality forces a change
• Keep notes—patterns emerge about what AI handles well and what it doesn't

4. When Things Go Wrong

Sometimes the AI gets stuck in a loop—fix one thing, break another, repeat. I've been there. When it happens:

1. Stop: No amount of small fixes will solve a fundamental misunderstanding
2. Give Context: The AI needs to know why things work the way they do
3. Know When to Reset: Sometimes the best fix is to:
   • Save the documentation changes (they're usually solid)
   • Revert the code
   • Start fresh with what you've learned

What Actually Works

Tools

• Cursor: It's faster and more capable than alternatives right now
• Copilot's Agent: Promising, but not quite there yet for complex work

Testing

• Write tests first—AI is great at implementation but terrible at knowing what to test
• Verify everything—AI's confidence is not correlated with correctness
• Don't move forward until tests pass—this isn't negotiable

Knowledge Management

• Document your action plan changes
• Track where AI consistently gets things wrong
• Build a playbook of successful patterns

The Reality for Different Developers

Senior Developers

The game is changing in our favor. We can finally:

• Focus on architecture instead of syntax
• Deliver business value instead of boilerplate
• Lead the integration of AI into development practices

Junior Developers

I won't sugarcoat it—the challenge is bigger now:

• The learning curve is steeper
• Code review skills matter more than ever
• Understanding why code works is crucial

The Truth About AI Coding

AI is great at:

• Turning requirements into initial code
• Implementing known patterns
• Writing documentation

But it still needs us for:

• Making architectural decisions
• Understanding business context
• Maintaining code quality

Moving Forward

This isn't about whether to use AI—that ship has sailed. It's about how to use it effectively. Start small, build your workflow, and expand as you learn what works for your team and codebase.

The goal isn't to replace developers—it's to let us focus on the interesting problems. The ones that require creativity, deep understanding, and human judgment.

The future of coding is already here. The question is: are you going to help shape it, or play catch-up later?