AI for Java Spring Boot Backend Engineers: Build, Deploy, and Ship Intelligent Features

(AI Integration is No Longer Optional — It's the Expected Skill for Modern Backend Engineers)

  • Every company is adding AI to their stack — Learn Spring AI integration through chatbots, document search,
    vector databases and much more.

  • Crack better roles or grow in your current company by implementing AI-enabled use cases that solve
    real business problems and prove your value.

  • Join other Java Spring Boot developers already shipping AI features — with hands-on code,
    interview questions, and projects.
What's inside?

6 Modules to Master Spring AI

From AI fundamentals to production-ready RAG systems. Each module builds on the previous one with hands-on projects and interview prep.

Module 1
🧠

AI Foundations

Understand LLMs, tokens, context windows, embeddings, temperature, and how AI models work under the hood.

What You Learn
Tokens & Pricing Context Windows Embeddings Temperature
Module 2
💬

Spring AI Core Features

Connect to OpenAI and Gemini, stream responses, manage conversations, and build with prompt templates.

What You Build
Streaming Chat UI Multi-Provider API Code Review Tool Conversation Memory
Module 3
🎯

Structured AI Output

Convert AI responses to Java objects. Build type-safe workflows with .entity() and ParameterizedTypeReference.

What You Build
Ticket Analyzer Auto-Escalation Priority Routing Response Generator
Module 4
⚡

Function Calling (Tools)

Let AI execute your Java methods. Build support bots that track orders, process cancellations, and handle refunds.

What You Build
Support Bot UI Order Tracking Cancel/Return Actions Multi-Tool Agent
Module 5
🛡️

Advisors (Middleware)

Build reusable interceptors for AI calls. Add logging, memory management, and PII redaction across all requests.

What You Build
Logging Advisor Memory Advisor PII Redaction Advisor Chain
Module 6
🔍

RAG (Vector Search)

Build AI systems powered by YOUR data. Ingest documents, store embeddings in pgvector, and create grounded AI assistants.

What You Build
RAG Assistant UI Document Ingestion Vector Search
Coming Soon

These 4 modules will be added at no extra cost. Price will increase after release.

🎙️

Audio Processing

🖼️

Image Generation

🔌

MCP (Model Context Protocol)

🤖

AI Agents

Total Course Content
6
Modules Now
+4
Coming Soon
25+
Video Lessons
5
UIs for Business Use cases
Hands-On Projects

Build 5 UIs for Business use cases

Not just theory - you'll build real, working AI applications with complete source code included

Module 2 💬

Streaming Chat UI

Real-time chat with word-by-word streaming. Switch between OpenAI and Gemini instantly.

SSE Streaming Multi-Provider Model Selection
Module 2 🔍

AI Code Review

Submit code for AI analysis. Get feedback on bugs, security, and best practices.

Multi-Language Prompt Templates Validation
Module 3 🎯

Ticket Analyzer

AI extracts priority, sentiment, category and generates escalation responses.

Structured Output Auto-Escalation .entity()
Module 4 🤖

AI Support Bot

AI that executes real actions - track orders, cancel, return, check refunds.

Function Calling @Tool Multi-Action
Module 6 📚

RAG Assistant

AI powered by YOUR data. Upload docs, vector search, grounded answers with sources.

Document Upload pgvector Citations

All UIs come with complete source code and detailed ReadMe file

Your Learning Roadmap

6 modules taking you from AI fundamentals to production RAG systems with lifetime access!

Module 1
🧠

AI Foundations

  • What are LLMs?
  • Tokens & Pricing
  • Context Windows
  • Embeddings Explained
  • Temperature & Parameters
Module 2
💬

Spring AI Core

  • ChatClient & ChatModel
  • Multi-Provider Setup
  • Streaming Responses (SSE)
  • Conversation Memory
  • Prompt Templates
  • Build: Streaming Chat UI
Module 3
🎯

Structured Output

  • .entity() Method
  • ParameterizedTypeReference
  • JSON Schema Generation
  • Enums & Complex Types
  • Conditional AI Calls
  • Build: Ticket Analyzer
Module 4
⚡

Function Calling

  • @Tool Annotation
  • @ToolParam Descriptions
  • Multi-Tool Agents
  • Error Handling
  • Build: Support Bot
  • Order/Cancel/Return Actions
Module 5
🛡️

Advisors (Middleware)

  • CallAdvisor Interface
  • Logging Advisor
  • Memory Advisor
  • PII Redaction
  • Advisor Ordering
  • Custom Advisor Chain
Module 6
🔍

RAG & Vector Search

  • Embedding Models
  • PostgreSQL + pgvector
  • Document Chunking
  • Similarity Search
  • Grounded Answers
  • Build: RAG Assistant

Complete all 6 modules and become a

Spring AI Expert - Ready for Production & Interviews!

Sneak Peek and Overview

Write your awesome label here.

Take Your Learning to the Next Level

Lifetime Access Plan
₹ 1,999
₹1,499
One-time payment • No subscriptions
  • Lifetime Access
  • Course Certificate
  • Unlimited Updates
  • Regularly Updated Content
Join the Course Now
Secure Payment via Instamojo • 100% Safe & Encrypted

Outside India? Pay via PayPal

Pay with PayPal

Course contents
(Click on any chapter below to open the Course Player)

Lifetime access 
video Tutorials
Hands-On Projects
Interview questions
Self-Paced Learning

Take Your Learning to the Next Level

Lifetime Access Plan
₹ 1,999
₹1,499
One-time payment • No subscriptions
  • Lifetime Access
  • Course Certificate
  • Unlimited Updates
  • Regularly Updated Content
Join the Course Now
Secure Payment via Instamojo • 100% Safe & Encrypted

Outside India? Pay via PayPal

Pay with PayPal
FAQs

Frequently Asked Questions

AI is no longer optional - it's becoming a core skill. Every major company is integrating AI into their products. As a backend engineer, you're in the perfect position to lead these initiatives.

Here's why this matters for your career:

1. Job Security: Engineers who can build AI-powered features are in high demand and harder to replace.
2. Internal Growth: Be the person your team turns to for AI projects - that's how promotions happen.
3. Future-Proofing: AI integration will be as common as database work in 2-3 years. Start now and stay ahead.
4. Higher Salaries: AI/ML engineering roles command 20-40% higher compensation.

The best part? You don't need to become a data scientist. Spring AI lets you use your existing Java/Spring skills to build powerful AI applications.

Absolutely not! This course is specifically designed for backend developers, not data scientists.

You won't be training models or doing complex math. Instead, you'll learn how to integrate pre-built AI models (like GPT-4, Gemini) into your Spring applications - just like you integrate databases or message queues.

If you can write a REST API in Spring Boot, you can build AI applications. Module 1 covers all the AI fundamentals you need - explained in terms developers understand.

Think of it this way: You don't need to understand how PostgreSQL's query optimizer works to use JPA. Similarly, you don't need to understand transformer architecture to use Spring AI.

By the end of this course, you'll be able to build production-ready AI features including:

1. Intelligent chatbots with streaming responses and conversation memory
2. AI-powered content analysis (code review, document summarization)
3. Support bots that execute real actions (check orders, process refunds)
4. RAG systems that answer questions from your company's documents
5. Smart ticket routing with sentiment analysis and auto-escalation

Very little - typically $2 to $5 for your handson practice and self learning.

OpenAI provides free credits for new users. Google Gemini has a generous free tier that covers most learning scenarios.

Cost-saving tip: Use gpt-4o-mini or gemini-flash models during learning. They're 10-20x cheaper than flagship models and perfect for development.

The course teaches you how to switch between providers, so you can use whichever offers better pricing for your use case.

Docker is only needed for Module 6 (RAG). Modules 1-5 work with just Java 17+ and your favorite IDE.

For Module 6, we use PostgreSQL with pgvector extension. A simple docker-compose up command sets everything up in seconds. Full instructions included.

System requirements:
- Java 17 or higher
- Any modern IDE (IntelliJ, VS Code, Eclipse)
- 8GB RAM recommended
- Docker Desktop (only for Module 6)

We're actively developing 4 advanced modules: Audio Processing, Image Generation, MCP (Model Context Protocol), and AI Agents.

If you enroll now, you'll get all 4 modules completely free when they're released. The course price will increase after each module launch.

Lock in current pricing: Enroll today at Rs 1,499 and get 10 modules total (6 now + 4 coming). After all modules release, the price will be significantly higher.

Yes! All videos are delivered in clear, easy-to-understand English, accessible to a global audience.

Yes! You'll receive a Certificate of Completion after finishing all modules. Great for your LinkedIn profile and showing employers you're AI-ready.

Spring AI is the Java/Spring equivalent of LangChain.

If you're already in the Spring ecosystem, Spring AI integrates naturally with your existing codebase, dependency injection, security, and Spring patterns you already know.

LangChain is Python-first. Spring AI is Java/Kotlin-first, backed by the official Spring team with enterprise-grade design patterns and production-ready abstractions.

Bottom line: If your company uses Spring Boot, Spring AI is the natural choice. No need to introduce Python just for AI features.

Your feedback helps us improve!

You can submit your thoughts through the Feedback & Suggestions section in the course. We read every submission and continuously update content based on student input.

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