I got tired of building the same CRUD applications.
So instead of creating another simple Employee Management System or To-Do application, I decided to build something that felt closer to a real product.
Over the last few weeks, I built three AI-powered applications using Spring Boot:
🤖 AI Code Reviewer for GitHub Pull Requests
📄 AI Resume Analyzer
💬 Self-Hosted AI Support Widget
Each project started with a simple idea:
Can I build something useful with the backend technologies I already know instead of just building projects for a tutorial?
The answer turned out to be yes.
But the most valuable part wasn't actually finishing the applications.
It was everything I learned while building them.
Why I Chose Spring Boot
I've been working with Java and Spring Boot, so I wanted to see how far I could take the stack when combined with AI.
When people talk about AI applications, the conversation often revolves around Python, machine learning models, notebooks, and data science.
But there is another side to AI:
The application layer.
You still need:
REST APIs
Authentication
Database integration
External API communication
Request validation
Error handling
File processing
Business logic
Logging
Configuration
Deployment
And that's where Spring Boot becomes extremely useful.
The AI model might generate the intelligence, but the backend is what turns that intelligence into an actual product.
Product 1: AI Code Reviewer for GitHub Pull Requests
The first product I built was an AI-powered code reviewer for GitHub Pull Requests.
The idea was simple:
Instead of manually going through every changed file in a Pull Request, why not let an AI analyze the changes and provide useful feedback?
The basic flow
GitHub Pull Request
↓
Fetch PR information
↓
Get changed code
↓
Prepare review prompt
↓
AI analysis
↓
Generate review
↓
Return useful feedback
The AI isn't just being asked:
"Is this code good?"
The goal is to make the review more useful by looking for things developers actually care about.
For example:
Potential bugs
Code quality problems
Security concerns
Performance issues
Maintainability
Bad practices
Possible improvements
What I Learned From This Project
The biggest lesson was:
AI output is only as good as the context you give it.
Initially, it is tempting to simply send a piece of code to an AI model and ask:
"Review this code."
But real code doesn't exist in isolation.
A method might depend on another class.
A variable might have a specific business meaning.
A change might only make sense when you look at the entire Pull Request.
So building an AI application isn't simply about calling an AI API.
It's about collecting the right context and presenting it in the right way.
That was one of my first major lessons.
Product 2: AI Resume Analyzer
The second product was an AI Resume Analyzer built with Spring Boot.
The idea came from something very familiar to anyone who has applied for jobs:
You spend hours creating a resume...
Then send it to a company...
And never know why it didn't get shortlisted.
So I wanted to build a tool that could analyze a resume and provide useful feedback.
The basic idea
Resume
↓
Upload
↓
Backend processing
↓
Extract resume content
↓
AI analysis
↓
Generate feedback
↓
Return analysis
The application can analyze areas such as:
Skills
Experience
Keywords
Resume structure
Job relevance
Missing information
Potential improvements
The interesting part was that this project introduced a completely different backend problem compared with the GitHub reviewer.
Now the application had to deal with documents and unstructured information.
What I Learned From This Project
This project taught me something important:
Building an AI application is often more about data preparation than the AI call itself.
You can't just throw a document at an AI model and expect perfect results.
The application needs to:
Receive the file
Process the content
Extract useful information
Organize that information
Send the right context to the AI
Process the response
Present the result in a useful format
The backend therefore becomes the bridge between the user's raw input and the AI model.
That made me appreciate Spring Boot even more.
Product 3: Self-Hosted AI Support Widget
This is the project I'm currently most excited about.
I built a self-hosted AI support widget using Spring Boot.
The idea was to create an AI-powered support experience without forcing someone to depend on an expensive monthly SaaS subscription.
The important word here is:
Self-hosted.
Instead of thinking:
"I need to subscribe to another SaaS product."
The idea becomes:
"I can run this myself."
The basic concept
User
↓
Support Widget
↓
Spring Boot Backend
↓
AI Service
↓
Response
↓
Support Widget
The backend handles the communication between the widget and the AI service.
That means the frontend doesn't need to contain all the business logic.
Spring Boot becomes the central layer responsible for the application's behavior.
Why I Like This Project
This project felt different from my previous applications because I wasn't just thinking about whether an API worked.
I started thinking about:
How would another developer use this?
How can the application be self-hosted?
What should the API look like?
How should errors be handled?
How should configuration work?
How can the application be extended later?
How can I make this feel like an actual product?
That's when I realized something:
There is a big difference between building an application and building a product.
What These 3 Projects Taught Me
After building all three, I noticed that the AI itself was not the hardest part.
Calling an AI API is relatively straightforward.
The difficult part is everything around it.
- AI Doesn't Replace Backend Engineering
This was probably my biggest takeaway.
AI can generate an answer.
But someone still has to build the system around it.
You still need to think about:
Authentication
↓
API Design
↓
Validation
↓
Business Logic
↓
Database
↓
External Services
↓
AI Integration
↓
Error Handling
↓
Deployment
That's backend engineering.
And AI becomes another service that the backend integrates with.
- Prompt Engineering Is Only One Piece
Before building these projects, I thought a lot about prompts.
After building them, I realized that prompt engineering is only one part of the problem.
A better mental model is:
Good Input
+
Good Context
+
Good Prompt
+
Good Backend Logic
=
Better AI Application
If the application sends poor or incomplete context, even a great model can produce a poor result.
- Product Thinking Matters
When you're following a tutorial, the goal is usually:
Make the application work.
When you're building your own product, the questions change.
You start asking:
Who is going to use this?
What problem does it solve?
Why would someone use this instead of another tool?
What happens when something fails?
How can I make it easier to use?
What features should I add later?
Those questions changed the way I approach development.
- Don't Build Features Just Because You Can
One of the easiest mistakes when building side projects is adding too many features.
You start with:
"I'll build an AI code reviewer."
Then suddenly you're planning:
Authentication
Teams
Billing
Analytics
Notifications
Dashboards
Admin panels
Multiple AI providers
Ten different integrations
And the original product never gets finished.
I've learned to ask:
Does this feature solve the core problem?
If the answer is no, it can wait.
- A Working Product Is More Valuable Than a Huge Project
I would rather have:
3 small working products
than:
1 huge unfinished application.
These projects also taught me that a project doesn't need 50 features to demonstrate engineering ability.
A relatively small application can demonstrate:
API design
Backend architecture
External integrations
AI integration
File processing
Error handling
Business logic
Deployment
Product thinking
That's already a lot.
The Tech Stack
Across these projects, my focus was primarily on the backend ecosystem I'm comfortable with.
Backend
Java
Spring Boot
REST APIs
Spring-based backend architecture
AI
AI APIs / LLM integration
Prompt design
Context preparation
AI-generated responses
Integrations
GitHub APIs
File/document processing
External services
Development
Maven
Git/GitHub
API testing
Logging
Configuration management
The exact implementation differs between the projects, but the common idea is the same:
Use Spring Boot as the application layer that connects users, business logic, external services, and AI.
What I Would Do Differently Next Time
If I started these projects again, I would spend more time on architecture before writing the first line of code.
I'd ask:
What is the core problem?
↓
What is the minimum useful product?
↓
What APIs do I need?
↓
What data do I need?
↓
Where does AI actually add value?
↓
How should the components communicate?
↓
How will I deploy it?
This prevents a project from turning into a collection of random features.
What's Next?
I'm not planning to stop at these three.
The next step is to take what I learned from these projects and build more practical backend tools around Java, Spring Boot, AI, and databases.
I'm particularly interested in projects that solve problems developers actually face.
Because my goal isn't simply:
"Build another AI project."
My goal is:
Build useful software and learn the engineering behind it.
Final Takeaway
Building these three products changed the way I look at AI development.
I started with:
"How do I integrate AI into a Spring Boot application?"
And ended up thinking much more about:
"How do I build a useful product where AI is actually part of the solution?"
That's a much more interesting question.
The AI model is only one component.
The real engineering challenge is everything around it.
And that's exactly the part I want to keep exploring.
The 3 Products
🤖 AI Code Reviewer for GitHub Pull Requests
Analyze Pull Request changes and generate AI-powered code review feedback.
📄 AI Resume Analyzer
Analyze resumes and provide AI-powered feedback and improvement suggestions.
💬 Self-Hosted AI Support Widget
Build an AI-powered support experience that can be self-hosted without relying entirely on a monthly SaaS platform.
If you're also a Java/Spring Boot developer experimenting with AI, I'd love to hear what you're building.
What would you build with Spring Boot + AI?
Drop it in the comments 👇
🔗 Projects
GitHub: [https://github.com/Sweety717/]
Project 1 — AI Code Reviewer: [https://swarnalata25.gumroad.com/l/codeguard-ai]
Project 2 — AI Resume Analyzer: [https://swarnalata25.gumroad.com/l/resumeiq-ai]
Project 3 — Self-Hosted AI Support Widget: [https://swarnalata25.gumroad.com/l/supportai-springboot]
