AI-Generated Code Auditing: Build a Static Analysis Framework That Catches What LLMs Get Wrong

What We're Building Let me show you a pattern I use in every project that involves AI-generated code: a repeatable audit framework. By the end of this workshop, you'll have a custom Detekt ruleset configured to catch the five most common anti-patterns LLMs produce, a review checklist calibrated for real codebases, and a CI gate that blocks the dangerous stuff before it ships. I built this after auditing a 40K-line Android + Kotlin backend codebase that was predominantly AI-generated. The team shipped fast — and then two senior engineers spent six weeks untangling the result. This framework is what I wish they'd had from day one. Prerequisites A Kotlin or Android project with Gradle Detekt added as a dependency (we'll cover setup if you haven't) Basic familiarity with CI/CD pipelines (GitHub Actions, GitLab CI, etc.) Roughly 20 minutes Step 1: Understand the Failure Patterns Before we configure tooling, you need to know what you're scanning for. Here are the five anti-patterns LLMs prod