📢LangChain4j got a new video series

We recently released a step-by-step course from simple chat to AI agents using LangChain4j 👉http://aka.ms/LangChain4j-for-Beginners

Now watch the new 6️⃣part Video series with tons of hands-on demos.

Let’s break down what you’ll learn.

1. Introduction to LangChain4j

Every journey starts with a working app. In this first session, you’ll connect to Azure OpenAI GPT-5, send your first prompts, and immediately see results. But the real insight comes when you add memory: watch a simple stateless demo transform into a production-ready conversational AI, side by side. Along the way, you’ll build intuition for tokens and context windows — the invisible constraints that shape everything your AI can do.

2. Prompt Engineering with LangChain4j

Now that your app is running, the question becomes: how do you ask the right questions? The same model gives wildly different results depending on how you prompt it. This session covers eight prompting patterns that control GPT-5’s reasoning depth — from quick calculations to deep architectural analysis. You’ll write self-reflecting prompts that iterate until code meets quality criteria, structured analysis frameworks for consistent reviews, and chain-of-thought techniques that make the AI’s reasoning visible.

3. Data-Driven Apps with RAG

Great prompts go a long way — but your AI still only knows what it learned during training. Retrieval-Augmented Generation (RAG) changes that. In this session, you’ll build a complete RAG pipeline: chunk documents, create semantic embeddings, and retrieve relevant context for every question. By the end, your AI answers questions about your own files with source citations and confidence scores — grounded in facts, not hallucinations.

4. Tools, MCP, and Agents

So far, your AI reads and responds. Now it’s time to make it act. You’ll expose Java methods as tools using @Tool annotations and watch the AI chain them automatically with the ReAct pattern. From there, you’ll explore the 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. This is where your AI stops being a text generator and becomes an action taker.

5. Safety, Reliability & Best Practices

An AI that can take action needs guardrails. This session is about building applications that are safe, reliable, and enterprise-ready. You’ll learn how to protect API keys and model endpoints, validate tool output, enforce content filters, and keep LLMs from stepping outside their intended boundaries. On the defensive side, you’ll design prompts that resist injection attacks, restrict system capabilities through structured interfaces, and implement patterns for secure RAG, safe memory handling, and audit-ready logging. The result: LangChain4j applications your team — and your users — can trust.

With special Guest Brian Benz 💖

6. Agentic Patterns

In this finale, we graduate from single agents to full multi-agent systems. You’ll explore eight patterns that power production AI: chain agents like an assembly line, fan-out for parallel expert opinions, loop until a critic approves, and route requests to the right specialist. Then go further with Supervisor agents that delegate like project managers and Human-in-the-Loop gates for when a person needs the final say. Finally, discover goal-oriented planners that find optimal paths and peer-to-peer meshes where agents collaborate as equals — no boss required.

With special Guest Mario Fusco 💖

Final Thoughts

In six sessions, you’ve gone from “Hello, AI” to orchestrating multi-agent systems.

Enjoy the above video series and explore, star and fork its repository👉 https://github.com/microsoft/LangChain4j-for-Beginners