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I recently had the opportunity to speak at Calgary BR Tech (CBRT) about a topic that is rapidly changing the Java landscape: integrating LLMs into our applications without the high costs or privacy concerns of external APIs. The "AI mystery" often feels tied to Python, but for those of us in the Java ecosystem, the tools have never been better. In this session, I broke down how to build a private, local, and data-aware AI system using: 🔹 Ollama: To run powerful models like Mistral locally on your hardware. 🔹 LangChain4j: The bridge that makes integrating LLMs into Java applications seamless. 🔹 RAG (Retrieval-Augmented Generation): Connecting the model to your own data (using PG Vector) to eliminate hallucinations and provide context-aware answers. The Highlight: We didn't just talk about implementation; we talked about Evaluation. I demonstrated how to use metrics like RAGAS and BLEU Score to measure context relevance and "faithfulness"—ensuring your AI actually knows what it’s talking about before it hits production. If you are a Java developer looking to step into the world of Generative AI using the frameworks you already know (like Quarkus and Spring), this talk is for you. 📺 Watch the full session, link in the comments. A huge thanks to the Calgary BR Tech team for hosting this event. It’s an exciting time to be a developer in Calgary! 🇨🇦 #Java #GenerativeAI #Ollama #LangChain4j #RAG #AI #SoftwareEngineering #Quarkus #CalgaryTech #JavaMapleLeaps