RAG demos are easy to build. Getting an AI system to consistently retrieve the right evidence is much harder.

When a RAG application gives a wrong answer, the instinct is often to blame the LLM. But many failures happen earlier: the right document was never retrieved, the relevant passage was ranked too low, the query did not match the user's intent, or semantic similarity simply wasn't enough.

This session goes behind the RAG demo and into the search problem underneath it. Using OpenSearch as the retrieval layer, we'll explore how lexical search, vector search and hybrid retrieval behave on real-world queries - and why relevance matters just as much as generation.

We'll look at practical failure cases, retrieval strategies, chunking and ranking decisions, and how to evaluate whether a RAG system is actually retrieving useful evidence rather than merely producing convincing answers.

The goal is not another “build a RAG app” tutorial. It is to show how to think like a search engineer when building AI applications - and how better retrieval can make the difference between an impressive demo and an AI system people can actually trust.