What I Learned Building an AI Code Reviewer with Java and Spring Boot

Wed, 14 Oct 2026 00:00:00 +0000

AI-assisted coding has changed how quickly developers can write software.

But that creates another question:

How do you review all that generated code effectively?

That question led me to build CodeGuard AI, a self-hosted AI code-review application for GitHub Pull Requests using Java and Spring Boot.

At first, the architecture seemed simple:

GitHub Pull Request
 ↓
 LLM
 ↓
 Review

In practice, that wasn’t enough. The interesting engineering work was everything around the model: collecting useful Pull Request context, defining review instructions, structuring the model response, assessing findings, and integrating the result back into the GitHub workflow. This article explains what I learned while building that workflow. The real problem isn’t calling an LLM Calling an AI provider from a Spring Boot application is relatively straightforward. The harder question is: What exactly should the model receive, and what should the application do with the response?

GitHub on foojay.io - Friends Of OpenJDK

What I Learned Building an AI Code Reviewer with Java and Spring Boot