AI Coding Workflow Checklist Before You Ship

AI coding tools can produce a working-looking app quickly. Cursor, GitHub Copilot, Claude Code, Codex, ChatGPT, Windsurf, Replit Agent, Bolt, and Lovable can all help draft code, fix errors, and reshape a project.

Shipping still needs a workflow. The real question is not whether AI can create code. The question is whether the result has been checked well enough to reach users.

Start with the scope of the AI change

Before reviewing a project, write down what the agent was asked to do:

• Was this a new app, a bug fix, a refactor, or a copied snippet?
• Which files did the agent edit?
• Did it touch auth, payments, data, deployment config, or SEO?
• Did it create tests, migrations, or environment variables?

This matters because AI tools often change nearby files while trying to complete a task. A small prompt can become a broad patch.

Review the diff before trusting the app

Open the changed files in your editor or git client. Look for:

• New dependencies
• Rewritten config
• Removed validation
• Hardcoded credentials or demo values
• Fake marketing copy or placeholder pages
• Tests that only mirror the implementation

Use a diff tool when you need to compare config, prompt text, API examples, README changes, or generated release notes.

Run the production path, not only the dev server

An AI-built app that runs locally can still fail during deployment. Run the command your host or build system will use, such as npm run build, next build, mvn package, flutter build, or the platform build step.

Record the output. If the command fails, keep the exact error for the next debugging pass instead of paraphrasing it.

Check secrets and deployment configuration

AI coding tools can invent variable names, move config into the wrong layer, or expose values to the client. Before publishing, check:

• .env, .env.example, and deployment variables
• API keys, service-role keys, webhook secrets, and test accounts
• Callback URLs, CORS rules, redirects, custom domains, and runtime versions
• Whether frontend-exposed variables are intentionally public

This is especially important for vibe-coded projects where the user may not know which values are safe to expose.

Test the risky product flows

Do not only click the happy path. Trigger the flows that commonly break after AI edits:

• Login, logout, expired session, and admin access
• Payment success, failure, retry, and webhook handling
• Empty states, invalid forms, failed network calls, and 404 pages
• Mobile layout at narrow widths
• Public SEO pages, canonical URLs, sitemap, robots rules, and Open Graph

If the project has no automated tests, write a short manual test record instead of pretending the gap does not exist.

Prepare the handoff note

A useful AI coding release note is short and factual:

• What changed
• What commands passed
• What was manually tested
• What was not tested
• Which risks remain
• How to roll back

That note helps a teammate, a future you, or the next AI agent continue with real context.

Use the checklist

Open the AI Coding Ship Checklist and choose the source, project type, stack, launch goal, and risky features. The tool generates Must, Should, and Optional checks you can complete locally and copy as Markdown.

It does not scan your repository or call an AI model. That is intentional for the first version: the checklist is a practical release gate you control.