Built for Working Professionals
Designed for developers who already know Java and want a clear, application-focused path into Generative AI — not academic ML theory.
For Experienced Java Developers
Java Gen AI Developer Training in ChennaiUpskill into Generative AISpring AI · RAG · MCP · Agents
Move beyond traditional Java development and become a Gen AI-ready engineer. This program helps working developers add practical Generative AI skills — from LLM fundamentals and embeddings to Spring AI, RAG, MCP, agents, observability, and production-ready evaluation.
- For Experienced DevsJava / Spring background
- Java + Gen AI FocusSpring AI & LLM apps
- RAG · MCP · AgentsProduction AI patterns
- Industry MentorsReal consulting experience
- Hands-on ProjectsPortfolio-ready Gen AI work
- Career UpskillingStay relevant in AI era
Ready to Upskill into Java + Gen AI?
Talk to our mentors about your current experience, learning goals, and how this Gen AI developer path can strengthen your next career move.
🚀 Upskill into Gen AI | ☕ Built for Experienced Java Developers | 🤖 Spring AI · RAG · MCP · Agents | 💼 Career-Focused Mentorship | 📞 Call: +91 72000 69003 | 💬 WhatsApp for Course Guidance
Why Experienced Developers Need Java Gen AI Skills
Product teams increasingly expect Java engineers to ship AI-assisted features. This Java Gen AI Developer Training in Chennai focuses on practical engineering skills you can apply in real Spring Boot systems.
Java-First Gen AI Stack
Learn Spring AI, embeddings, RAG with Qdrant, tool calling, MCP, and agent patterns that fit naturally into enterprise Java architectures.
Career-Relevant Depth
Cover local and cloud LLMs, observability, evaluation, and production concerns so you can discuss and deliver Gen AI features confidently.
What You Will Learn in Java Gen AI Developer Training
A complete, hands-on curriculum that takes experienced Java developers from AI fundamentals to production Gen AI systems with Spring AI.
AI Fundamentals
- What is AI?
- AI vs Machine Learning vs Deep Learning
- Generative AI
- Traditional AI vs Generative AI
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning — basic introduction
- What is an LLM?
- How an LLM generates a response
- Next-token prediction
- Training vs inference
- Parameters of an LLM
- Context window
- Tokens
- Tokenization
- Why token count matters
- Temperature and randomness — concept
- Hallucination
Embeddings & Vector Concepts
- What are embeddings?
- Text → Vector
- Vector dimensions
- Semantic similarity
- Cosine similarity
- Vector search
- Static embeddings
- Positional embeddings
- Modern embedding approaches
- Embeddings vs LLM
- Why vectors are important for RAG
Understanding LLM Providers
- Open-source vs closed-source LLM
- Cloud LLM vs local LLM
- Model vs API
- Model size
- 7B / 8B / 14B / 70B models
- CPU vs GPU inference
- Quantization — basic concept
Local LLM
- Setting up a local open-source LLM server
- Running an LLM locally
- Calling LLM through REST API
- Understanding request/response
- Model management
Docker for LLM Infrastructure
- Setting up LLM using Docker
- Persistent model storage
- GPU support
- Container networking
- Java application → Dockerized LLM
Cloud LLM Infrastructure
- Running LLM infrastructure in AWS
- EC2 + LLM
- GPU instances
- API-based cloud LLM
- Cost considerations
- Local vs cloud architecture
Introduction to Spring AI
- What is Spring AI?
- Spring AI architecture
- Spring AI vs direct REST API
- Spring Boot + Spring AI
- Project setup
- Configuration
- Model providers
- Hello World with Spring AI
Spring AI Core APIs
- ChatModel
- ChatClient
- Prompt
- UserMessage
- SystemMessage
- AssistantMessage
- ChatResponse
Multiple LLMs
- OpenAI
- Anthropic
- Gemini
- Ollama
- Azure OpenAI
- Multiple model configuration
- Model selection at runtime
Prompt Engineering with Spring AI
- What is a prompt?
- System prompt
- User prompt
- Assistant response
- Roles in LLM
- Implementing Roles in Spring AI
Prompt Templates
- Static prompts
- Dynamic prompts
- Variables
- PromptTemplate
- Reusable prompts
- Prompt versioning
Prompt Stuffing
- What is prompt stuffing?
- Adding external context
- Large context problems
- Context window limitations
- Prompt injection
- Context prioritization
ChatOptions & Generation Controls
- Temperature
- Top-P
- Top-K
- Frequency penalty
- Presence penalty
- Stop sequences
- Maximum output tokens
Spring AI Response
- ChatResponse
- ChatClientResponse
- Content
- Metadata
- Usage
- Model information
- Finish reason
Entity Output & OutputConverter
- Text response
- POJO response
- JSON response
- Structured output
- Mapping AI response → Java object
- What is OutputConverter?
- String → Java object
- List response
- Custom converter
- Validation
Tokens, Usage & Cost
- Input tokens
- Output tokens
- Total tokens
- Context window
- Token cost
- Why prompts become expensive
- Token optimization
- Token usage in Spring AI
- ChatResponse metadata
- Token tracking
- Cost calculation
- Usage logging
Advisors
- What is an Advisor?
- Advisor lifecycle
- Advisor chain
- Before LLM call
- After LLM call
- Token usage with Advisors
- Logging
- Memory Advisor
- RAG Advisor
- Logging Advisor
- Token tracking Advisor
- Custom Advisor
Conversation Memory
- What is chat memory?
- Conversation history
- Conversation ID
- Memory Advisor
- Message storage
- Limiting chat history
- Context window management
- Short-term memory
- Long-term memory — concept
- Database-backed memory
- MessageChatMemoryAdvisor
- Chat memory repository
- Custom memory implementation
RAG Fundamentals
- What is RAG?
- Why RAG?
- LLM knowledge limitations
- RAG architecture
- Retrieval
- Augmentation
- Generation
- RAG vs fine-tuning
Vector Database & Qdrant
- Vector database
- Vector storage
- Embeddings
- Similarity search
- Metadata
- Filtering
- Top-K search
- Introduction to Qdrant
- Installation
- Docker setup
- Collections
- Vectors
- Similarity search with Qdrant
- Filtering in Qdrant
Document Processing & Chunking
- Loading documents
- PDF, DOCX, TXT, HTML, Markdown
- Document parsing
- Document metadata
- Why chunk?
- Fixed-size chunking
- Token-based chunking
- Sentence-based chunking
- Recursive chunking
- Chunk overlap
- Chunk size selection
- Metadata preservation
- Chunking quality
Build RAG with Spring AI
- Document ingestion
- Document → chunks
- Chunks → embeddings
- Embeddings → Qdrant
- User question → embedding
- Similarity search
- Retrieved context
- LLM response
Advanced RAG & Semantic Cache
- RAG preprocessing
- RAG postprocessing
- Metadata filtering
- Hybrid search
- Query transformation
- Query rewriting
- RAG Web Search
- Traditional cache vs semantic cache
- Embedding-based cache
- Similar-question detection
- Cache hit/miss
- Cost and latency reduction
Function / Tool Calling
- What is tool calling?
- Function calling
- Tool definition
- Tool parameters
- Tool selection
- Tool execution
- Tool result
- Final response
- AI + Database
- AI + REST API
Model Context Protocol (MCP)
- Why MCP?
- Tool calling vs MCP
- MCP architecture
- MCP Client
- MCP Server
- MCP Tools
- MCP Resources
- MCP Prompts
- Create MCP Server with Spring AI
- Expose tools, parameters, and responses
- Security
- Create MCP Client
- Discover and invoke tools
- Connect to 3rd-party MCP servers
- STDIO and HTTP-based transport
- Remote MCP architecture
- Sampling, elicitation, auth, and tool isolation
AI Agent Fundamentals
- What is an AI Agent?
- Agent vs chatbot
- Agent vs tool calling
- Agent loop
- Planning
- Tool selection
- Observation
- State
- Memory
- Multi-step execution
- Agent limitations
- Agent safety
AI Beyond Text — Speech & Image
- Speech-to-text
- Audio transcription
- Voice input
- Text-to-speech
- Voice assistant architecture
- Image understanding
- Vision models
- Image → AI
- AI → Image
- Prompt-to-picture
Spring AI Observability
- Why AI observability?
- Request tracking
- Response tracking
- Latency
- Token usage
- Model usage
- Error tracking
- Tool execution tracking
- RAG monitoring
- Metrics
- Prometheus
- Grafana
Testing AI Applications
- Why traditional assertions do not work
- Deterministic vs non-deterministic testing
- AI evaluation
- Response relevance
- Factual correctness
- Groundedness
- Toxicity
- Hallucination detection
- RelevanceEvaluator
- FactCheckingEvaluator
- Evaluation datasets
- RAG evaluation
- Retrieval evaluation
- Answer evaluation
Hands-on Gen AI Projects
Apply the curriculum through projects that reflect how companies bring Generative AI into Java applications.
Spring AI Chat Application
Build a multi-provider chat service with prompts, memory advisors, structured output, and token usage tracking.
Enterprise RAG with Qdrant
Ingest documents, chunk content, create embeddings, store vectors in Qdrant, and answer questions with retrieval-augmented generation.
Tool Calling & MCP Agent
Connect LLMs to Java APIs and databases using tool calling and MCP, then orchestrate multi-step agent workflows with guardrails.
Who Should Join This Course?
This Java Gen AI Developer program is a strong fit if you are:
- Working Java / Spring Boot developers looking to upskill into Gen AI
- Backend engineers who want to build LLM-powered product features
- Full stack developers aiming for AI-enabled application roles
- Tech leads exploring practical Gen AI architecture for their teams
If you are a beginner seeking full stack foundations, explore our Java Full Stack Training – Pay After Placement program first.
Outcomes After Completing the Program
Design and ship Gen AI features inside Java / Spring Boot applications
Build RAG pipelines with embeddings, chunking, and Qdrant
Use Spring AI for prompts, memory, advisors, tools, and MCP
Evaluate, observe, and productionise AI features with confidence
Learn from Industry Mentors
Mathanlal Sait
Founder | Senior Software Consultant | Java Full Stack & Gen AI Mentor
Training professionals for IT careers with 16+ years of software consulting experience — focused on practical skills that working developers can apply immediately.
View Trainer Testimonials on LinkedInHow to Apply
Share your resume and current experience. We will confirm fit for this experienced-developer Gen AI track and guide you on the next steps.
1
Profile Review
Send your resume to +91 72000 69003 on WhatsApp with a short note on your Java experience.
Apply Now2
Counselling & Confirmation
Discuss goals, course structure, and batch timing. Confirmation typically follows within 1–2 working days.
Frequently Asked Questions
1. Who is the Java Gen AI Developer course designed for?
Experienced Java developers and working professionals who want to upskill into Generative AI application development using Spring Boot and modern LLM tooling.
2. Do I need prior machine learning experience?
No. AI fundamentals are covered early, then the focus shifts to practical Gen AI engineering — Spring AI, RAG, MCP, agents, observability, and evaluation.
3. What will I learn in this Java Gen AI training?
You will learn LLM fundamentals, embeddings, local and cloud model setup, Spring AI APIs, prompt engineering, advisors, memory, RAG with Qdrant, tool calling, MCP, agents, multimodal AI, observability, and AI testing.
4. Is this suitable for Java beginners?
This track assumes working Java experience. Beginners should start with our Java Full Stack programs before moving into Gen AI upskilling.
5. Will I get mentor support for project work?
Yes. Mentorship is part of the learning process so you can clarify design decisions, improve prompts and pipelines, and complete portfolio-ready projects.
6. How is this different from a general ChatGPT workshop?
This is an engineering course. You learn to design, integrate, evaluate, observe, and ship Gen AI features inside Java applications — not only how to write prompts in a chat UI.
Start Your Java Gen AI Upskilling Journey
If you are an experienced developer ready to become a Java Gen AI Developer, we are happy to help you plan the next step with clear guidance and practical training.