Enterprise GenAI for Java Developers

Course Description

This course provides a comprehensive, enterprise-grade approach to building AI-powered applications using Java and Spring AI. Participants will learn how to design, develop, and deploy intelligent systems leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), multimodal AI, and agentic workflows.

The course emphasizes real-world enterprise use cases such as document processing, customer support automation, and secure AI integration, while also addressing critical aspects like cost optimization, observability, and responsible AI practices.

Who is this course for

• Java Developers and Spring Boot Engineers

• Backend Engineers building AI-powered applications

• AI/ML Engineers transitioning to enterprise application development

• Software Architects designing GenAI solutions

• Developers working on automation, chatbots, or internal tools

Course Objectives

By the end of this course, participants will be able to:

• Understand core LLM concepts like tokenization, context windows, and temperature

• Build enterprise-grade AI applications using Spring AI

• Implement structured output extraction from LLMs

• Develop multimodal applications (text, image, audio)

• Design and implement RAG pipelines with vector databases

• Build AI agents capable of function calling and tool usage

• Apply Responsible AI practices including safety, privacy, and hallucination control

• Monitor, evaluate, and deploy production-ready AI systems

Prerequisites

• Basic knowledge of Spring Boot

• Familiarity with REST APIs and JSON

• Basic understanding of AI/ML concepts (helpful but not mandatory)

• Docker basics

• IDE: IntelliJ IDEA or VS Code

Course outline

Section 1: The Enterprise AI Ecosystem & Infrastructure

1. Introduction to Enterprise AI Architecture

2. LLM Mechanics in Java (Tokenization, Context Window, Temperature)

3. Spring AI Architecture and Core Abstractions

4. Spring AI vs LangChain4J

5. Infrastructure Strategies: GitHub Models, Hugging Face, Azure OpenAI

6. Multi-Provider Strategy for Development vs Production

7. Environment Configuration using application.yml

8. Docker Setup for AI Development

9. GitHub Codespaces Setup

Hands-On Labs:

• HOL: The Token & Cost Calculator (JTokkit-based utility)

• HOL: The Provider Switcher (Spring Profiles with Ollama & Azure/OpenAI)

Section 2: Advanced Logic Extraction & Multimodality

1. ChatClient Fluent API (Spring AI 1.0+)

2. Prompt Engineering for Developers

3. Chain-of-Thought (CoT) and Few-Shot Prompting

4. Structured Output Extraction using BeanOutputConverter

5. JSON and POJO Mapping using MapOutputConverter

6. Handling the "Logic Gap" in LLM Outputs

7. Introduction to Multimodal AI

8. Vision Models (GPT-4o Vision) for Document Processing

9. Audio Processing with Whisper

Hands-On Labs:

• HOL: Brand Content Studio (Blog + Image Generator using DALL·E)

• HOL: B2B Receipt Extractor (Invoice → Structured Java Record)

• HOL: Transcription Summarizer (Whisper + MeetingMinutes POJO)

• HOL: Dialogue Summarizer using Hugging Face

Section 3: Enterprise RAG (Retrieval-Augmented Generation)

1. Introduction to RAG Architecture

2. ETL Pipeline for AI (DocumentReader, PDF/JSON ingestion)

3. Text Chunking with TokenTextSplitter

4. Vector Embeddings and Storage Concepts

5. Vector Databases: PGVector and Redis

6. Similarity Search and Ranking

7. Multitenancy in RAG Systems

8. Metadata Filtering for Secure Retrieval

Hands-On Labs:

• HOL: Working with Postgres Vector DB (PGVector setup)

• HOL: Policy Expert (HR Manual RAG System)

• HOL: Multi-tenant Vector Search (Department-based filtering)

Section 4: Agentic Actions & MCP (Model Context Protocol)

1. Introduction to AI Agents

2. Function Calling in Spring AI

3. Registering Java Methods as AI Tools

4. Model Context Protocol (MCP) Fundamentals

5. Tool Reasoning Loop (Reasoning → Action → Observation)

6. Building Secure Agent Workflows

7. Running Agents in Offline / Secure Environments

Hands-On Labs:

• HOL: Intelligent DB Agent (Natural Language → SQL queries)

• HOL: MCP Logistics & Supply Chain Service

• HOL: Human-in-the-Loop Agent Workflow

Section 5: Responsible AI & Enterprise Safety

1. Introduction to Responsible AI

2. Content Safety: Hard Blocks vs Soft Refusals

3. Prompt Injection and Jailbreaking Attacks

4. Securing AI Applications in Java

5. PII Detection and Redaction Strategies

6. Hallucination Detection and Mitigation

7. Grounding AI with Verified Context

Hands-On Labs:

• HOL: Responsible AI Red Teaming Exercise

• HOL: Privacy Guardrail (PII Redaction using Spring AI Advisor)

• HOL: Hallucination Scorer (Cross-verification using smaller models like Phi-3)

Section 6: Production Engineering & Final Capstone

1. Observability in AI Systems (Micrometer & Actuator)

2. Monitoring Token Usage, Latency, and Errors

3. Evaluation Techniques (AI-as-a-Judge)

4. Semantic Caching Concepts

5. Redis-based Semantic Cache Implementation

6. Deployment Strategies for GenAI Applications

7. Cloud Deployment (Azure, AWS, OCI)

8. Performance Optimization and Cost Control

Hands-On Labs:

• HOL: AI Observability Dashboard (Prometheus + Grafana)

• HOL: Semantic Cache Optimizer (Redis-based caching)

Section 7: Capstone Project

• B2B Customer Support Agent