RAG unlocks many of the most useful AI product experiences by grounding model output in real context: docs, tickets, codebases, and domain data. Most teams can build a demo quickly. The challenge is making it reliable, fast, and maintainable in production.
This session breaks RAG into practical architectural choices: retrieval strategy (keyword, vector, hybrid, graph), indexing and freshness, ranking, prompt assembly, and evaluation. We’ll also cover where teams get stuck with related patterns like memory and agentic RAG, and how to decide when they actually help versus add complexity.
You’ll leave with a decision framework for choosing the right RAG approach per use case, plus concrete implementation patterns to improve answer quality, reduce hallucinations, and keep latency and cost under control.