AI & LLMs
What is AI code review?
AI code review is the use of an AI model to examine code for bugs, security weaknesses, and quality problems, and to explain what it finds.
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Technical definition
AI code review applies a code-trained model to analyze source code and report defects, risks, and deviations from good practice. It covers logic errors, unhandled cases, exposed credentials, injection vulnerabilities, missing access controls, performance problems, and inconsistency with the surrounding codebase.
AI review differs from static analysis in that it reasons about intent rather than matching rules, which lets it find issues no rule anticipated and also lets it report issues that are not real.
In plain terms
You hand code to an AI and ask what is wrong with it. It tells you, in words you can read.
The useful part for a non-technical builder is the explanation. A traditional scanner reports a rule violation on line 40. An AI review says what the problem is, what could happen, and how to fix it.
Why it matters
When AI writes your code, review is the quality control you have left. Nobody else is reading it.
AI review is dependable on the categories that follow recognizable patterns: a secret in client-side code, a database rule that lets any user read any row, input that reaches a query without validation, an error path that goes nowhere. Those are exactly the mistakes that matter in a small business app, and exactly the ones a non-technical builder cannot spot.
What it will not tell you is whether the product is right. It cannot know that your pricing tiers are backwards, or that the confirmation email should go to the customer rather than to you. That check belongs to you, on the running app.
It also produces false positives. A flagged issue is a question to look into, not a verdict.
