Retrieval-Augmented Generation (RAG) is the gold standard for grounding LLMs in proprietary enterprise data. However, the journey from a flashy proof-of-concept to a reliable, secure production system is fraught with complexity. This talk bridges the gap between rapid prototyping and rigorous engineering. We will begin by exploring the actual merit of “vibe-coding”—using AI-assisted, rapid prototyping to build compelling demo apps that secure client buy-in and validate use cases.

Once the demo is sold, the real work begins. We will navigate the architectural decision matrix, comparing the trade-offs of off-the-shelf products, cloud-managed services, and fully custom-built RAG solutions. Diving into the technical pipeline, we will cover modern ingestion strategies like agentic chunking and sophisticated retrieval methods, including re-ranking and GraphRAG, to overcome the limitations of simple vector search. We will detail the essential role of telemetry and LLM-as-a-Judge evaluation frameworks in ensuring sustained performance, faithfulness, and relevancy. Join us to learn how to transition from impressive demos to robust solutions that deliver measurable business value.