AI Generated Code Best Practices Before You Deploy

AI coding tools have made it possible to write large amounts of code very quickly. That speed is the point — but it creates a new challenge: how do you maintain quality and security when a significant portion of your codebase was written by an AI in minutes?

The developers who get the most out of AI coding tools aren't the ones who accept everything without looking at it. They're the ones who have a clear process for reviewing and validating AI output before it reaches production.

Understand What You're Shipping

This is rule one. If you can't explain what a piece of AI-generated code does, it shouldn't be in your production codebase.

This doesn't mean you need to understand every implementation detail before deployment — it means you understand the intent, the inputs, the outputs, and the main edge cases. Ask your AI tool to explain the code if you're unclear:

Explain this function line by line and describe what would happen
if the input array were empty, contained duplicates, or contained null values.

Review All Diffs Before Accepting

In Cursor, every inline edit shows a diff. In code review, every PR shows a diff. Reading diffs carefully is the single most important habit when working with AI-generated code.

Pay attention to:

• What was removed, not just what was added — sometimes AI silently deletes validation or error handling
• Edge case handling — does the new code handle null, empty, out-of-range inputs?
• Error handling — are exceptions caught appropriately or swallowed silently?

Test AI-Generated Code Thoroughly

Write tests for the code before deploying it. Better yet, ask the AI to write the tests first and then check the implementation against them:

Write the test cases for a function that validates a user's email address.
Cover: valid email, missing @ symbol, missing TLD, empty string, null, unicode characters.

Then ask it to implement the function, and run the tests. If the tests pass, you have some confidence the implementation is correct. If they fail, iterate.

Security-Sensitive Code Needs Extra Review

AI tools can generate insecure code — not maliciously, but because they pattern-match from a vast training set that includes bad code alongside good code.

Areas that need manual security review:

SQL and database queries: Check for potential injection vulnerabilities even when using ORMs. Verify that raw queries use parameterised inputs.

Authentication and authorisation: AI-generated auth code can be logically correct but miss edge cases like expired tokens, concurrent session invalidation, or privilege escalation.

Input validation: Verify that all user-supplied data is validated before use, not just the obvious fields.

Secrets handling: Ensure API keys, passwords, and tokens aren't being logged, returned in responses, or stored insecurely.

Review this authentication handler for security issues,
paying attention to timing attacks, token storage, and error messages
that might leak information about valid usernames.

Don't Deploy Without Running It First

This sounds obvious but is worth stating: run the code locally before deploying. Cursor and other AI editors will produce syntactically valid code that fails at runtime — missing environment variables, wrong method signatures from a library update, database schema mismatches.

Run through the critical user paths manually after any significant AI-generated changes.

Keep AI Changes Small and Focused

Smaller AI-generated diffs are easier to review than large ones. A Composer change that touches 15 files simultaneously is hard to review thoroughly. A targeted change to 2-3 files is manageable.

If you need a large change, break it into phases:

1. Ask AI to implement phase 1 (data model changes)
2. Review, test, deploy phase 1
3. Ask AI to implement phase 2 (API layer changes)
4. Review, test, deploy phase 2

This keeps each deployment's blast radius small and makes bugs easier to attribute.

Use Type Checking and Static Analysis

AI-generated code benefits enormously from static analysis tools. Run your linter, type checker, and static analysis tools after AI makes changes:

# PHP
./vendor/bin/phpstan analyse
./vendor/bin/pint

# TypeScript
tsc --noEmit

# Python
mypy .

These tools catch a category of errors that AI produces fairly often — incorrect type usage, undefined variables, unreachable code.

Document Why, Not Just What

AI can generate code that does something. But it can't document why your team made a particular choice, what alternatives were considered, or what business constraint this implements. Add those comments yourself — they're the context that future developers (and future AI tools) need to make good decisions.

Monitor What You Deploy

Even code that passes all your checks can behave unexpectedly in production under real traffic and real data. Uptime monitoring is your safety net — it detects when AI-generated code causes a production issue fast enough to limit the damage.

Domain Monitor checks your site every minute and alerts you immediately if it starts returning errors. Combine that with structured logging in your application and you can diagnose production issues from AI-generated code quickly.

See also: monitoring apps built with AI tools for a broader look at production monitoring for AI-assisted development.

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