{"id":232928,"date":"2026-03-24T00:28:32","date_gmt":"2026-03-24T07:28:32","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/java\/?p=232928"},"modified":"2026-03-24T00:28:32","modified_gmt":"2026-03-24T07:28:32","slug":"%f0%9f%93%a2langchain4j-got-a-new-video-series","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/java\/%f0%9f%93%a2langchain4j-got-a-new-video-series\/","title":{"rendered":"\ud83d\udce2LangChain4j got a new video series"},"content":{"rendered":"<p><a href=\"https:\/\/devblogs.microsoft.com\/java\/wp-content\/uploads\/sites\/51\/2026\/03\/LangChain4j.webp\"><img decoding=\"async\" class=\"alignnone wp-image-232930\" src=\"https:\/\/devblogs.microsoft.com\/java\/wp-content\/uploads\/sites\/51\/2026\/03\/LangChain4j-1024x683.webp\" alt=\"LangChain4j image\" width=\"537\" height=\"358\" srcset=\"https:\/\/devblogs.microsoft.com\/java\/wp-content\/uploads\/sites\/51\/2026\/03\/LangChain4j-1024x683.webp 1024w, https:\/\/devblogs.microsoft.com\/java\/wp-content\/uploads\/sites\/51\/2026\/03\/LangChain4j-300x200.webp 300w, https:\/\/devblogs.microsoft.com\/java\/wp-content\/uploads\/sites\/51\/2026\/03\/LangChain4j-768x512.webp 768w, https:\/\/devblogs.microsoft.com\/java\/wp-content\/uploads\/sites\/51\/2026\/03\/LangChain4j.webp 1536w\" sizes=\"(max-width: 537px) 100vw, 537px\" \/><\/a><\/p>\n<p>We recently released a step-by-step course from simple chat to AI agents using LangChain4j \ud83d\udc49<a href=\"http:\/\/aka.ms\/LangChain4j-for-Beginners\">http:\/\/aka.ms\/LangChain4j-for-Beginners<\/a><\/p>\n<p>Now watch the new 6\ufe0f\u20e3part <strong>Video<\/strong> series with tons of hands-on demos.<\/p>\n<p>Let&#8217;s break down what you&#8217;ll learn.<\/p>\n<ol>\n<li><strong> Introduction to LangChain4j<\/strong><\/li>\n<\/ol>\n<p>Every journey starts with a working app. In this first session, you&#8217;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&#8217;ll build intuition for tokens and context windows \u2014 the invisible constraints that shape everything your AI can do.<\/p>\n<p><iframe src=\"\/\/www.youtube.com\/embed\/nl_troDm8rQ?list=PLIvYLozy_ihfaiUfJzgI_cfzGgywc6gKK&amp;index=2\" width=\"560\" height=\"314\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<ol start=\"2\">\n<li><strong> Prompt Engineering with LangChain4j<\/strong><\/li>\n<\/ol>\n<p>Now that your app is running, the question becomes:\u00a0<em>how do you ask the right questions?<\/em>\u00a0The same model gives wildly different results depending on how you prompt it. This session covers eight prompting patterns that control GPT-5&#8217;s reasoning depth \u2014 from quick calculations to deep architectural analysis. You&#8217;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&#8217;s reasoning visible.<\/p>\n<p><iframe src=\"\/\/www.youtube.com\/embed\/PJ6aBaE6bog?list=PLIvYLozy_ihfaiUfJzgI_cfzGgywc6gKK&amp;index=3\" width=\"560\" height=\"314\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<ol start=\"3\">\n<li><strong> Data-Driven Apps with RAG<\/strong><\/li>\n<\/ol>\n<p>Great prompts go a long way \u2014 but your AI still only knows what it learned during training. Retrieval-Augmented Generation (RAG) changes that. In this session, you&#8217;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\u00a0<em>your own files<\/em>\u00a0with source citations and confidence scores \u2014 grounded in facts, not hallucinations.<\/p>\n<p><iframe src=\"\/\/www.youtube.com\/embed\/_olq75ZH_eY?list=PLIvYLozy_ihfaiUfJzgI_cfzGgywc6gKK&amp;index=3\" width=\"560\" height=\"314\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<ol start=\"4\">\n<li><strong> Tools, MCP, and Agents<\/strong><\/li>\n<\/ol>\n<p>So far, your AI reads and responds. Now it&#8217;s time to make it\u00a0<em>act<\/em>. You&#8217;ll expose Java methods as tools using\u00a0@Tool\u00a0annotations and watch the AI chain them automatically with the ReAct pattern. From there, you&#8217;ll explore the\u00a0Model Context Protocol (MCP)\u00a0\u2014 an open standard for AI-to-tool communication \u2014 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.<\/p>\n<p><iframe src=\"\/\/www.youtube.com\/embed\/O_J30kZc0rw?list=PLIvYLozy_ihfaiUfJzgI_cfzGgywc6gKK&amp;index=4\" width=\"560\" height=\"314\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<ol start=\"5\">\n<li><strong> Safety, Reliability &amp; Best Practices<\/strong><\/li>\n<\/ol>\n<p>An AI that can take action needs guardrails. This session is about building applications that are safe, reliable, and enterprise-ready. You&#8217;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&#8217;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 \u2014 and your users \u2014 can trust.<\/p>\n<p>With special Guest Brian Benz \ud83d\udc96<\/p>\n<p><iframe src=\"\/\/www.youtube.com\/embed\/Wvn_n0rv1ZE?list=PLIvYLozy_ihfaiUfJzgI_cfzGgywc6gKK&amp;index=5\" width=\"560\" height=\"314\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<ol start=\"6\">\n<li><strong> Agentic Patterns<\/strong><\/li>\n<\/ol>\n<p>In this finale, we graduate from single agents to full multi-agent systems. You&#8217;ll explore eight patterns that power production AI:\u00a0chain\u00a0agents like an assembly line,\u00a0fan-out\u00a0for parallel expert opinions,\u00a0loop\u00a0until a critic approves, and\u00a0route\u00a0requests to the right specialist. Then go further with\u00a0Supervisor\u00a0agents that delegate like project managers and\u00a0Human-in-the-Loop\u00a0gates for when a person needs the final say. Finally, discover\u00a0goal-oriented planners\u00a0that find optimal paths and\u00a0peer-to-peer meshes\u00a0where agents collaborate as equals \u2014 no boss required.<\/p>\n<p>With special Guest Mario Fusco \ud83d\udc96<\/p>\n<p><iframe src=\"\/\/www.youtube.com\/embed\/3q1bb_12928?list=PLIvYLozy_ihfaiUfJzgI_cfzGgywc6gKK&amp;index=6\" width=\"560\" height=\"314\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<p><strong>Final Thoughts<\/strong><\/p>\n<p>In six sessions, you&#8217;ve gone from &#8220;Hello, AI&#8221; to orchestrating multi-agent systems.<\/p>\n<p>Enjoy the above video series and explore, star and fork its repository\ud83d\udc49 <a href=\"https:\/\/github.com\/microsoft\/LangChain4j-for-Beginners\">https:\/\/github.com\/microsoft\/LangChain4j-for-Beginners<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We recently released a step-by-step course from simple chat to AI agents using LangChain4j \ud83d\udc49http:\/\/aka.ms\/LangChain4j-for-Beginners Now watch the new 6\ufe0f\u20e3part Video series with tons of hands-on demos. Let&#8217;s break down what you&#8217;ll learn. Introduction to LangChain4j Every journey starts with a working app. In this first session, you&#8217;ll connect to Azure OpenAI GPT-5, send your [&hellip;]<\/p>\n","protected":false},"author":29534,"featured_media":232930,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-232928","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-java"],"acf":[],"blog_post_summary":"<p>We recently released a step-by-step course from simple chat to AI agents using LangChain4j \ud83d\udc49http:\/\/aka.ms\/LangChain4j-for-Beginners Now watch the new 6\ufe0f\u20e3part Video series with tons of hands-on demos. Let&#8217;s break down what you&#8217;ll learn. Introduction to LangChain4j Every journey starts with a working app. In this first session, you&#8217;ll connect to Azure OpenAI GPT-5, send your [&hellip;]<\/p>\n","_links":{"self":[{"href":"https:\/\/devblogs.microsoft.com\/java\/wp-json\/wp\/v2\/posts\/232928","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/devblogs.microsoft.com\/java\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/devblogs.microsoft.com\/java\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/java\/wp-json\/wp\/v2\/users\/29534"}],"replies":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/java\/wp-json\/wp\/v2\/comments?post=232928"}],"version-history":[{"count":1,"href":"https:\/\/devblogs.microsoft.com\/java\/wp-json\/wp\/v2\/posts\/232928\/revisions"}],"predecessor-version":[{"id":232947,"href":"https:\/\/devblogs.microsoft.com\/java\/wp-json\/wp\/v2\/posts\/232928\/revisions\/232947"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/java\/wp-json\/wp\/v2\/media\/232930"}],"wp:attachment":[{"href":"https:\/\/devblogs.microsoft.com\/java\/wp-json\/wp\/v2\/media?parent=232928"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/java\/wp-json\/wp\/v2\/categories?post=232928"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/java\/wp-json\/wp\/v2\/tags?post=232928"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}