AI Coding Workflow

Check and ship AI-generated code.

After an agent edits your code, verify diffs, builds, environment variables, and release settings locally.

Workflow checkpoints

01

Before AI edits

Define the task, target files, constraints, acceptance criteria, and what the agent should not touch.

  • Write a concrete task brief
  • Name risky areas like auth, payments, and data
  • Keep secrets out of prompts
02

During AI edits

Watch the diff, run build or tests, capture terminal errors, and ask the agent to explain risky changes.

  • Review file changes
  • Run build and smoke tests
  • Package errors with reproduction steps
03

After AI edits

Prepare the project for release: environment variables, deployment settings, README, changelog, rollback notes, and mobile checks.

  • Check env and API keys
  • Verify deploy settings
  • Record release notes and smoke tests

Tools

Ship AI-generated changes

Generate a local checklist for build, env vars, auth, payments, SEO, mobile, docs, and release notes.

Review AI code changes

Check scope, behavior, security, tests, dependencies, deployment assumptions, and follow-up questions before merge.

Debug AI-built apps

Turn errors, reproduction steps, recent AI changes, and attempted fixes into a packet for the next AI or developer.

Compare AI changes

Use text diff when an agent rewrites config, copy, prompts, or API responses and you need to see what moved.

Inspect API output

Use JSON, URL, JWT, and cURL tools when AI-generated code fails around requests, tokens, or payload shape.

Prepare release docs

Preview Markdown for README, changelog, issue reports, and release notes before you publish or hand off.

Guides and courses

Verify agent changes, manage prompt context, and ship with a checklist.

Verification gates for AI coding agents

How to decide whether an AI agent change is ready to keep, rerun, or reject.

Context engineering for AI agents

How to give coding agents enough project context without pasting secrets or irrelevant files.

AI workflow checklist for developers

A structured lesson for using AI without losing context, privacy, or verification.