How I Built an AI Code Reviewer for GitHub Pull Requests with Spring Boot

How I Built an AI Code Reviewer for GitHub Pull Requests with Spring Boot

TL;DR: I built an AI-powered GitHub Pull Request reviewer with Spring Boot that receives GitHub webhooks, fetches code changes, sends relevant context to an AI model, and generates an automated first-pass code review.

Code reviews are one of those things every development team needs.

But they're also repetitive.

A developer opens a Pull Request.

Someone reviews it.

They look for bugs, security issues, code quality problems, performance concerns, and possible improvements.

So I started wondering:

What if AI could perform the first round of review before a human reviewer even looks at the PR?

That idea led me to build an AI-powered GitHub Pull Request Code Reviewer using Spring Boot, GitHub APIs, and an AI model.

The goal wasn't to replace human code reviews.

It was to automate the boring first pass and give developers useful feedback earlier.

The Architecture

The basic workflow looks like this:

Developer creates Pull Request
|
v
GitHub Webhook
|
v
Spring Boot API
|
v
Fetch Pull Request data
|
v
Extract code diff
|
v
Build AI prompt
|
v
AI API call
|
v
Generate review
|
v
Store / return result

The interesting part wasn't simply calling an AI API.

The real challenge was connecting multiple systems and making the workflow reliable as a backend service.

Why Spring Boot?

I chose Spring Boot because it provides a clean way to build REST APIs and integrate external services.

The backend is responsible for:

Receiving GitHub webhook events
Processing Pull Request information
Communicating with GitHub APIs
Extracting code changes
Communicating with the AI service
Handling failures and timeouts
Processing the generated review

A simplified architecture looks like this:

             GitHub
               |
               | Webhook
               v
    +-----------------------+
    |      Spring Boot      |
    |        Backend        |
    +-----------------------+
          |           |
          |           |
          v           v
    GitHub API       AI API
          |           |
          +-----+-----+
                |
                v
          Review Result

Step 1: Receiving the GitHub Webhook

Instead of continuously polling GitHub for new Pull Requests, I wanted GitHub to notify my application whenever something happened.

For example, when a Pull Request is opened or updated, GitHub can send a webhook request to the Spring Boot application.

A simplified controller looks like this:

@RestController
@RequestMapping("/webhook")
public class GithubWebhookController {

@PostMapping("/pull-request")
public ResponseEntity<String> handlePullRequest(
        @RequestBody String payload) {

    // Process GitHub webhook

    return ResponseEntity.ok("Webhook received");
}

}

Of course, a production implementation shouldn't simply accept any incoming request.

The webhook should be validated, the payload should be parsed properly, and the event type should be handled accordingly.

This gave me an important reminder when working with external integrations:

Never blindly trust incoming webhook requests.

Step 2: Getting Pull Request Details

Once the webhook is received, the application needs information about the Pull Request.

For example:

Repository
Pull Request number
Source branch
Target branch
Author
Changed files

The backend can then communicate with GitHub's API to retrieve the required information.

Conceptually:

Webhook
|
v
Repository + PR Number
|
v
GitHub API
|
v
Pull Request Details
|
v
Changed Files / Diff

This is where the project starts becoming more than a simple CRUD application.

We're integrating with an external system, transforming its data, and feeding it into another service.

Step 3: Extracting the Code Changes

One thing I didn't want to do was send an entire repository to the AI model.

That would introduce unnecessary context and potentially increase processing cost.

Instead, the application focuses primarily on the changes introduced by the Pull Request.

For example:

• return user.getName();
• return user.getName().trim();

The AI doesn't always need the entire application.

It needs enough relevant context to understand:

What changed?

and

Could this change introduce a problem?

This makes the review more focused.

Step 4: Building the AI Prompt

This turned out to be one of the most important parts.

Simply sending:

Review this code.

doesn't give the AI enough direction.

Instead, I give it a specific role and review criteria.

For example:

You are a senior Java code reviewer.

Review the following Pull Request changes.

Look for:

1. Bugs
2. Security vulnerabilities
3. Performance problems
4. Exception handling issues
5. Code quality problems
6. Maintainability issues

For every significant issue:

• Explain the problem
• Explain why it matters
• Suggest a possible improvement

If there are no significant issues, say so clearly.

Focus only on meaningful issues.
Do not suggest unnecessary changes.

Pull Request diff:

[DIFF HERE]

The important lesson here was:

The quality of an AI response depends heavily on the quality of the context and instructions you provide.

Prompting isn't just about asking a question.

It's about giving the model the right context to reason about.

Step 5: Calling the AI Service

From Spring Boot, the application can communicate with an external AI API using an HTTP client.

Conceptually:

public String reviewCode(String codeDiff) {

String prompt = buildPrompt(codeDiff);

// Send prompt to AI service
// Receive generated review
// Return review

return review;

}

But the backend responsibility doesn't end with making the API call.

A real application also needs to think about:

API failures
Timeouts
Invalid responses
Rate limits
Authentication
Logging
Retry strategies

This is where the project became more interesting from a backend engineering perspective.

The Complete Flow

Putting everything together:

Developer
|
| Creates / updates PR
v
GitHub
|
| Webhook
v
Spring Boot
|
| Validate event
v
Extract PR information
|
| GitHub API
v
Get changed files / diff
|
v
Build AI prompt
|
| AI API
v
Generate review
|
v
Process response
|
v
Store / return review

What started as an idea for an AI code reviewer became a backend workflow involving multiple systems.

The Biggest Thing I Learned

The most interesting part of this project wasn't actually the AI API.

It was everything around it.

AI is only one component of the system.

A production-style application still needs:

Clean API design
Authentication
Input validation
Error handling
External API integration
Logging
Security
Rate limiting
Observability
Good database design when persistence is required

You can have a powerful AI model, but if your backend fails when GitHub sends a webhook, the application isn't very useful.

AI doesn't remove backend engineering. It makes good backend engineering even more important.

What I'd Improve Next

There are several things I'd add if I continued developing this project.

1. Asynchronous Processing

AI requests can take time.

Instead of keeping the webhook request waiting, I'd move the review process into an asynchronous workflow.

GitHub
|
v
Webhook
|
v
Spring Boot
|
v
Queue
|
v
Worker
|
v
AI Review

This would make the webhook endpoint faster and provide a better foundation for scaling.

1. Review History

I'd store previous reviews so developers could see:

Previous issues
Repeated problems
Review history
Changes in code quality over time

1. Configurable Review Rules

Different teams care about different things.

For example:

Java
Spring Boot
Security
Performance
Clean Code
Architecture

The review criteria could be configurable depending on the project.

1. Better Context Management

This is one of the biggest challenges with AI-based developer tools.

Send too little context and the AI may misunderstand the change.

Send too much context and the response becomes less focused while increasing processing cost.

So the real engineering question becomes:

What is the minimum context the AI needs to produce a useful review?

I think that's a much more interesting problem than simply connecting an application to an AI API.

AI Doesn't Replace the Developer

I don't see AI code review as a replacement for human developers.

I see it as a first-pass reviewer.

AI can quickly identify potential problems.

But a human developer still needs to ask:

Is this actually a problem for our application?

That's particularly important for business logic and architectural decisions, where the AI may not have enough context.

Final Thoughts

Building this project changed the way I think about AI applications.

The interesting part isn't:

"I connected Spring Boot to an AI API."

The interesting part is building a reliable backend system around that AI capability.

For me, the combination of:

Java + Spring Boot + APIs + AI + real-world automation

is much more interesting than building another basic CRUD application.

And that's the kind of backend engineering I want to keep exploring.

What would you add?

If you were building an AI-powered Pull Request reviewer, what would you prioritize?

Security vulnerabilities? Performance issues? Architecture? Code quality?

I'd love to hear what other developers would add.

Comments

[Luis Cruz]

Your approach to utilizing Spring Boot for handling GitHub webhooks and integrating AI for initial code reviews is quite insightful. The focus on building a reliable backend service while ensuring that webhooks are properly validated is a critical detail that often gets overlooked. One potential improvement could be implementing retries for failed AI API calls to enhance the robustness of the system. If you’re seeking additional support with scaling this application or enhancing its features, I’d be glad to explore a paid collaboration. What challenges have you encountered when fine-tuning the AI model for code review accuracy?

[JavaCoder7]

Thanks, Luis! I really appreciate your thoughtful feedback.
I completely agree about retries they're one of the improvements I'd add alongside timeout handling and asynchronous processing to make the workflow more robust.
Regarding AI accuracy, the biggest challenge wasn't fine-tuning the model itself, but providing the right context from the Pull Request. Choosing how much of the diff and surrounding code to send, while keeping the review focused and avoiding unnecessary suggestions, was the most interesting problem. Prompt design and context selection had a much bigger impact on the quality of the review than I initially expected.
I'd definitely be interested in exploring ideas around scaling the architecture and improving the reviewer further. Thanks again for taking the time to read the article!