LangChain4j for Beginners

5.00 PM - 6.00 PM (UTC)

Build your first AI-powered Java application from scratch. You'll connect to Azure OpenAI GPT-5, send your first prompts, and discover why memory transforms a simple demo into a production-ready conversational AI. See the dramatic difference between stateless and stateful chat side-by-side, and learn how tokens and context windows shape everything your AI can do.

5.00 PM - 6.00 PM (UTC)

The same AI gives wildly different results based on how you ask. Master eight prompting patterns that control GPT-5's reasoning depth, from quick calculations to deep architectural analysis. Learn to write self-reflecting prompts that iterate until code meets quality criteria, structured analysis frameworks for consistent reviews, and chain-of-thought techniques that reveal the AI's reasoning process.

5.00 PM - 6.00 PM (UTC)

Your AI only knows what it learned during training—until you give it access to your documents. Build a complete Retrieval-Augmented Generation system that chunks documents, creates semantic embeddings, and finds relevant context for every question. Watch your AI answer questions about your own files with source citations and confidence scores, grounded in facts rather than hallucinations.

5.00 PM - 6.00 PM (UTC)

Move from "text generator" to "action taker." You'll expose Java methods as tools using @Tool annotations and watch the AI chain them automatically with the ReAct pattern. Then you'll explore Model Context Protocol (MCP)—an open standard for AI-to-tool communication—and build a Supervisor Agent that dynamically orchestrates sub-agents to read files, analyze content, and summarize results

4.00 PM - 5.00 PM (UTC)

Learn how to build safe, reliable, and enterprise-ready AI applications in Java, including how to protect API keys and model endpoints, validate tool output, enforce content filters, and keep LLMs from stepping outside their intended boundaries. See how to design prompts defensively, restrict system capabilities, and use structured interfaces to avoid injection attacks. We'll also explore patterns for safe RAG, secure memory, and audit-ready logging that make your Langchain4j applications trustworthy, enterprise-grade, and ready for real users.Learn how to build safe, reliable, and enterprise-ready AI applications in Java, including how to protect API keys and model endpoints, validate tool output, enforce content filters, and keep LLMs from stepping outside their intended boundaries. See how to design prompts defensively, restrict system capabilities, and use structured interfaces to avoid injection attacks. We'll also explore patterns for safe RAG, secure memory, and audit-ready logging that make your Langchain4j applications trustworthy, enterprise-grade, and ready for real users.