Java Full Stack + Generative AI Engineering
GenAI Engineering – Morning Batch | Nov 2026
About This Course
Java Full Stack + Generative AI Engineering is a hands-on program for building modern, AI-powered applications with Java. The first part covers the full stack you need: modern Java, SQL and PostgreSQL, Spring Boot REST APIs and a React frontend. The larger second part is about Generative AI engineering: working with large language models, Spring AI, embeddings and vector search, Retrieval-Augmented Generation (RAG), tool calling and AI agents, and running AI features in production.
You finish by building and deploying a full-stack AI application of your own.
What You'll Learn
- Modern Java: OOP, collections, streams, records and virtual threads
- SQL, PostgreSQL and pgvector
- Spring Boot REST APIs, Spring Data JPA and Spring Security with JWT
- Testing with JUnit and Mockito
- React with TypeScript, including a streaming chat interface
- How large language models work: tokens, context and cost
- Prompt engineering and calling LLM APIs from Java
- Spring AI: chat clients, prompt templates, structured output and chat memory
- Embeddings, vector search and document ingestion
- Retrieval-Augmented Generation (RAG): building, improving and evaluating it
- Tool calling, AI agents and the Model Context Protocol (MCP)
- Guardrails, prompt-injection defence and handling personal data
- Cost, latency, caching, observability and testing for AI applications
- Docker, deployment and CI/CD
Hands-On Learning
- Live coding in every session
- Weekly assignments
- AI features built step by step on one growing application
- Code reviews
- A capstone project you deploy and present
Projects
AI Document Assistant
A RAG application that answers questions over PDFs and documents, with citations.
AI Agent with Tools
An agent that uses your own database and APIs to complete multi-step tasks safely.
Capstone: Full-Stack AI Application
A deployed Spring Boot + React application with AI features of your choice, presented in a final demo.
Who Should Join?
- BE / B.Tech, BCA, MCA and BSc Computer Science / IT students
- Final-year students and recent graduates
- Java developers who want to add Generative AI skills
- Anyone aiming for a backend, full-stack or AI engineering role
Prerequisites
Basic programming knowledge in any language is required. Java is taught from the essentials, at a fast pace.
Course Outcome
By the end of the course you can build, secure, test and deploy full-stack Java applications with Generative AI features such as RAG search, chat assistants and tool-using agents, and you have a deployed project for your portfolio.
Curriculum · 12 modules, 56 lessons
Module 1 — Java Essentials 6 lessons
Core Java, fast and practical, as the base for everything that follows.
- Java ecosystem, JDK and JVM, IDE setup and your first program · 90
- Variables, data types, operators and control flow · 90
- Object-oriented programming: classes, objects and encapsulation · 90
- Interfaces, inheritance, polymorphism and abstract classes · 90
- Collections and generics: List, Set and Map · 90
- Lambdas, streams and exception handling · 90
Module 2 — Modern Java for AI Applications 3 lessons
The modern Java features AI applications rely on.
- Records, Optional, text blocks and modern Java syntax · 90
- Concurrency, CompletableFuture and virtual threads for parallel AI calls · 90
- Java HTTP client, JSON with Jackson and calling web APIs · 90
Module 3 — SQL and PostgreSQL 4 lessons
Relational data and the vector extension used later for AI search.
- SQL fundamentals: tables, keys, CRUD and filtering · 90
- Joins, aggregation, indexes and transactions · 90
- Spring Data JPA: entities, repositories and relationships · 90
- PostgreSQL with pgvector: setup and storing vectors · 90
Module 4 — Spring Boot and REST APIs 6 lessons
Production-style backend services.
- Spring Boot, dependency injection, configuration and profiles · 90
- Building REST controllers, DTOs and request validation · 90
- Spring Data JPA in a layered service · 90
- Global error handling and testing with JUnit and Mockito · 90
- Spring Security and JWT authentication · 90
- API documentation with OpenAPI and testing with Postman · 90
Module 5 — React Frontend 5 lessons
A modern UI for AI-powered applications.
- JavaScript and TypeScript essentials for React · 90
- React components, props, state and hooks · 90
- Forms, routing and calling REST APIs · 90
- Building a streaming chat interface · 90
- State management, loading states and UX polish · 90
Module 6 — LLM Fundamentals 4 lessons
How large language models work and how to use them from Java.
- How LLMs work: tokens, context windows, models and cost · 90
- Prompt engineering: system prompts, few-shot and prompt patterns · 90
- Calling LLM APIs from Java · 90
- Streaming responses to the browser · 90
Module 7 — Spring AI 4 lessons
The Spring way to build AI features.
- Spring AI ChatClient and prompt templates · 90
- Structured output: mapping model responses to Java records · 90
- Chat memory and conversation history · 90
- Images and multimodal input · 90
Module 8 — Embeddings and Vector Search 3 lessons
Semantic search over your own data.
- Embeddings and similarity search · 90
- pgvector as a Spring AI vector store · 90
- Ingesting PDFs and documents: parsing and chunking · 90
Module 9 — Retrieval-Augmented Generation (RAG) 5 lessons
Answers grounded in your documents and data.
- RAG architecture and when to use it · 90
- Building a RAG API with Spring AI · 90
- Improving retrieval: chunking, metadata filters and re-ranking · 90
- Citations and reducing hallucinations · 90
- Evaluating RAG answers · 90
Module 10 — Tool Calling and AI Agents 5 lessons
Models that act: call your code, data and services.
- Function and tool calling · 90
- Building an agent that uses your database and APIs · 90
- Model Context Protocol (MCP): servers and clients · 90
- Multi-step workflows and planning · 90
- Guardrails, human approval and safe actions · 90
Module 11 — Production Generative AI 4 lessons
Running AI features reliably, safely and affordably.
- Cost, latency, caching and rate limits · 90
- Security: prompt injection, PII and output validation · 90
- Testing and observability for LLM applications · 90
- Docker, deployment and CI/CD for AI applications · 90
Module 12 — Capstone Project and Placement Readiness 7 lessons
Build, deploy and present a full-stack AI application.
- Capstone design: requirements, architecture and sprint plan · 90
- Capstone: backend services and data model · 90
- Capstone: RAG and agent features · 90
- Capstone: React interface · 90
- Capstone: testing, security review and documentation · 90
- Capstone: deployment · 90
- Project demo, mock interview and resume review · 90
Cancellation & refund policy
Refund Policy
- If a cancellation request is received 7 days or more before the batch start date, 25% of the course fee will be deducted as cancellation charges and the remaining amount will be refunded.
- If a cancellation request is received between 3 and 6 days before the batch start date, 50% of the course fee will be deducted as cancellation charges and the remaining amount will be refunded.
- If a cancellation request is received less than 3 days before the batch start date, no refund will be applicable.
- No refund will be provided once the batch has started.
- If the batch is cancelled by ConfidentHire, the full course fee paid by the student will be refunded.
- Course fees are non-transferable to another student.
- Approved refunds will be processed within 7–10 working days.
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