Uploaded April 2026 | Updated September 2026, 2 weeks ago
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Enterprise and government document systems hold terabytes of valuable unstructured information, yet most still rely on keyword and metadata search with little semantic context. Retrieval-Augmented Generation (RAG) promises a breakthrough, but tutorials rarely prepare you for regulated, large-scale environments.
In this talk, I’ll share lessons from building a RAG stack with Spring Boot, Elasticsearch, LangChain4j, Docker, and ActiveMQ, using both Azure OpenAI and Ollama. Expect concrete learnings on document chunking, enforcing access control, and keeping LLMs grounded in facts. Practical takeaways for anyone bringing RAG from demo to production.
Please subscribe to our YouTube channel @ youtube.com/@DevoxxForever
Subscribe to LinkedIn @ linkedin.com/company/voxxed-days-amsterdam
Follow us on Twitter @ twitter.com/voxxedamsterdam
Enterprise and government document systems hold terabytes of valuable unstructured information, yet most still rely on keyword and metadata search with little semantic context. Retrieval-Augmented Generation (RAG) promises a breakthrough, but tutorials rarely prepare you for regulated, large-scale environments.
In this talk, I’ll share lessons from building a RAG stack with Spring Boot, Elasticsearch, LangChain4j, Docker, and ActiveMQ, using both Azure OpenAI and Ollama. Expect concrete learnings on document chunking, enforcing access control, and keeping LLMs grounded in facts. Practical takeaways for anyone bringing RAG from demo to production.
