Vector RAG that actually understands context
Basic vector RAG breaks down as systems scale. Semantic similarity alone cannot enforce hierarchy, causality, or typed relationships - leading to degraded accuracy and opaque results over time.
In this live webinar, Matthew Penaroza (Head of Solutions Architecture, SurrealDB) shows how to design retrieval systems that maintain context as data and usage grow. You’ll learn how vectors and graphs work together to enforce structure, reduce search space, and explain why evidence was retrieved - not just that it was. This session focuses on retrieval quality and interpretability, not database internals.
In this session you'll learn
- Why vector-only RAG fails at scale (and why common fixes don’t solve it)
- What “context” actually means in retrieval systems
- How graph-based retrieval complements vectors
- Implementing hybrid vector + graph retrieval in a single query
- Producing retrieval traces that explain evidence selection
- What to measure to avoid semantic collapse in production
