A RAG application can generate a convincing answer from the wrong evidence.

When a retrieval system fails, the problem is often blamed on the LLM. But the model can only work with the information it receives. Poor chunking, weak metadata, an ineffective query, irrelevant retrievals or incorrect ranking can silently remove the evidence needed to answer correctly.

This session treats RAG as a retrieval engineering problem rather than simply an LLM integration pattern.

We will follow a question through query formulation, retrieval, ranking, context construction and generation, examining where information can be lost at each stage. We will compare lexical, semantic and hybrid retrieval strategies and discuss chunking, metadata, reranking and retrieval evaluation.

Most importantly, we will separate retrieval quality from generation quality so that teams can identify whether a poor answer is actually an LLM problem or a data and retrieval problem.

Attendees will leave with a practical framework for designing and evaluating RAG systems that retrieve useful evidence consistently rather than simply returning more documents.