Java + LLMs  + LangChain4j — 2025 Talk Series

Shaaf and I have been heads‑down exploring how LangChain4j slots into everyday Java and Jakarta EE projects. Our experiments have grown into a full talk series.

You can find a list of delivered and upcoming talks on my conference page: https://bazlur.ca/conferences/

Why we’re doing this

• LangChain4j gives Java devs RAG pipelines, vector‑store abstractions, and agent helpers without leaving the JVM.
• Jakarta EE supplies the familiar plumbing—CDI, JPA, JAX‑RS—so LLM features drop into existing codebases instead of sitting in sidecars.
• Together they let us prototype AI‑powered features (chat, summarization, semantic search), Function calling, MCP and many more. You can take them straight to production.

What the session covers

• Quick introduction to LLM plumbing in Java
• Prompt design patterns
• Memory management techniques
• Tool integration (function calling)
• RAG (Retrieval‑Augmented Generation) end‑to‑end
• vector stores
• Model Context Protocol

Slides: https://speakerdeck.com/bazlur_rahman/java-plus-llms-a-hands-on-guide-to-building-llm-apps-in-java-with-jakarta-334970cb-c9e9-46ff-931b-65b0a7a50adb

Try the code

We built a progressive demo repo — https://github.com/learnj-ai/llm-jakarta .

We’re excited to keep refining these ideas and would love your feedback—see you at the next stop on the schedule!

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