Retrieval-Augmented Generation is everywhere, and most teams start with vector search when building out these agents. It works well when the goal is finding relevant text. It struggles when the task shifts to understanding, summarizing, or reasoning across multiple documents. Developers often discover this the hard way when their system retrieves "relevant" chunks but still produces shallow, inconsistent, or even contradictory answers.

This session introduces Hybrid RAG as a practical alternative. We'll walk through how combining vector retrieval with symbolic and keyword-based approaches changes what the model can actually do. You'll see why Hybrid RAG performs better for synthesis-heavy tasks, how it reduces failure modes common in embedding-only pipelines, and how to implement it in practice. The talk includes multiple live demos that show the differences side by side, using open-source code you can adapt to your own solutions.