The Data Exchange with Ben Lorica Why ‘Structure’ Is All You Need: A Deep Dive into Next-Gen AI Retrieval
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Feb 13, 2025 
Tom Smoker, co-founder of WhyHow.ai, shares insights on transforming unstructured data into structured knowledge. He discusses how knowledge graphs enhance AI retrieval, citing successful applications at LinkedIn and Pinterest. The conversation delves into the complexities of building these graphs and the balance between automation and intentional design. They also tackle the hallucination problem in AI, emphasizing the role of data clarity. Smoker argues for leveraging operational data to improve AI systems, showcasing the potential of graph-based methodologies.
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Graph RAG Definition
- Graph RAG uses a graph or knowledge graph component in retrieval augmented generation (RAG).
- There's no consensus on a strict definition, with varying design patterns for knowledge graph integration.
Graph RAG and Structured Data
- Tom Smoker's definition of Graph RAG depends on context, focusing on retrieval from structured data.
- Structured queries on structured data allow for more consistent and compressed retrieval.
Using Structure in RAG
- Leverage structure like SQL databases or metadata even without a full knowledge graph.
- Infuse RAG queries with queries from structured data sources for improved retrieval.