
Moving Beyond RAG with Precomputed Context
- 访谈
- 科技
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
,
the fly. The approach borrows the database concept of a materialized view and curates
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B2 · 中高级
能看懂电视新闻就会觉得轻松
- 80% 的单词在 B1 或更简单的水平。
- 每分钟 168 词:自然语速,偏快。
- 整集共有 215 个较难的单词(B2 及以上)。
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较难的单词(B2+)
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 developed Nexus, which is a knowledge engine that reframes context as a first-class, pre-computed asset rather than something reassembled on
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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