Developing software with AI chat: a practical loop

Over the last few months, AI chat has become part of my daily development loop. It’s not replacing design or architecture decisions, but it is accelerating the boring parts and tightening feedback. Here’s the workflow I’ve settled into.

1) Start with constraints, not features

I open with a short brief that includes:

• Target environment (Next.js export, serverless, etc.)
• Non‑negotiables (security, accessibility, bundle size)
• Input/output types and data boundaries

This keeps the conversation grounded and prevents overly clever but unsafe suggestions.

2) Ask for a plan before code

I ask for a step‑by‑step plan and validate it against the repo. It’s faster to correct a plan than to unwind a thousand lines of code.

3) Work in tight iterations

I keep changes small and run checks after each step. If a change can’t be explained in one sentence, it’s probably too big.

4) Review like a human, not a compiler

I look for:

• Naming and clarity
• Hidden complexity
• Edge cases around error states

If the AI introduces something I can’t explain to a teammate, it doesn’t ship.

5) Capture what works

Good prompts become snippets in my notes. A small library of “known good” prompts saves hours over time.

Prompt template:
Context: (stack + constraints)
Goal: (clear outcome)
Inputs/outputs: (types)
Non‑goals: (explicit exclusions)

The result: faster iteration without giving up ownership of the codebase. AI chat is a sharp tool when you keep the handle in your hand.