Techniques

How to Use AI for Code Review and Quality

Jay Banlasan

Jay Banlasan

The AI Systems Guy

tl;dr

Automated code review that catches bugs, security issues, and style violations. AI-powered code quality.

Using ai code review quality checks before deploying automation scripts catches bugs that would have crashed your system at 2 AM. You do not need to be a senior developer. You need AI looking over the code.

The Use Case for Non-Developers

If you build automations with AI assistance (and you should be), you are writing code you may not fully understand. That is fine. But deploying code you do not understand without review is risky.

AI code review bridges the gap. Paste your script, ask AI to review it for bugs, security issues, and edge cases. It explains what it finds in plain English.

What AI Catches

Logic errors: "This loop will run forever because the counter never increments."

Security issues: "This script stores the API key in plain text. Move it to an environment variable."

Edge cases: "If the API returns an empty list, this function will crash because it tries to access the first element without checking."

Performance problems: "This queries the database inside a loop. With 1,000 items, that is 1,000 database calls. Batch them into one query."

The Review Prompt

Give AI the code and ask specific questions. "Review this script for: bugs that would cause a crash, security issues with credentials, edge cases with empty or unexpected data, and anything that would fail in production but work in testing."

Specific questions produce specific answers. "Review this code" gets generic feedback.

Claude Code for Live Review

With Claude Code, you get an even tighter loop. Write the code, let Claude review it, fix the issues it finds, and test the result. All in one session. The review happens before deployment, not after the first production failure.

Making It a Habit

Review every script before deployment. Every time. Even the small "quick fix" scripts. Especially the small quick-fix scripts. Those are the ones that skip testing and cause problems.

Set up a pre-deployment checklist: write the code, run AI review, fix flagged issues, test with sample data, then deploy. Five steps that prevent most production issues.

Build These Systems

Ready to implement? These step-by-step tutorials show you exactly how:

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