Moving Beyond RAG with Precomputed Context
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Moving Beyond RAG with Precomputed Context

B2Upper-intermediateSeptember 3, 202655 min
  • Interview
  • Technology

Retrieval has become one of the central problems in building useful AI systems. The standard approach to grounding a model in one’s own data has been retrieval augmented generation, or RAG, where an agent searches a vector database for relevant information at query time. That pattern works, but it has limitations, such as retrieving information that’s not truly relevant, repeating the same lookup work on every query, and producing inconsistent answers to the same question. Pinecone is a vector database that’s widely used to power semantic search and RAG at scale. The team recently develo...

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search and RAG at scale. The team recently developed Nexus, which is a knowledge engine

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the fly. The approach borrows the database concept of a materialized view and curates

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Retrieval has become one of the central problems in building useful AI systems. The standard approach to grounding a model in one's own data has been Retrieval Augmented Generation, or RAG, where an agent searches a vector database for relevant information at query time. That pattern works, but it has limitations, such as retrieving information that's not truly

0:23

relevant, repeating the same lookup work on every query, and producing inconsistent answers to the same question. Pinecone is a vector database that's widely used to power semantic search and RAG at scale. The team recently developed Nexus, which is a knowledge engine that reframes context as a first-class, pre-computed asset rather than something reassembled on

0:46

the fly. The approach borrows the database concept of a materialized view and curates context once into a versioned artifact that carries its own schema, metadata, permissions, and lineage. Jorg Schad is the VP of Engineering at Pinecone. In this episode, he joins Kevin Ball for an in-depth conversation about the frontier of retrieval technology. They discuss

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