Unverified50% confidenceFactExact time
Vector search compares semantic similarity and is inherently poor at exact matching (such as numbers, dates, and codes); directed queries are a weakness of vector search.
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50%
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Long-term
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10/15/2026
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Related Claims
UnverifiedPure vector search falls short for information with high requirements for structure and precision (such as names and dates), and needs to be supplemented with metadata filtering77% similarUnverifiedVector retrieval performs terribly at looking up exact identifiers (such as contract numbers, customer names, date ranges, and material codes), because embedding models compress text into dense vectors, and similarity ranking has no binding force for exact equality.76% similarUnverifiedRelying solely on vector similarity retrieval often yields imprecise recall, necessitating a hybrid retrieval approach that combines keyword-based retrieval.75% similarUnverifiedThe bottleneck of single-pass retrieval stems from vector similarity search compressing the user query into a single high-dimensional vector; when a question contains multiple semantic directions, this vector becomes an average of multiple semantics, causing sub-intents to be insufficiently represented.72% similarUnverifiedThe shortcomings of Naive RAG include: single vector retrieval tends to overlook exact keyword matching, there is no metadata filtering, and the lack of reranking results in insufficiently refined relevance ordering.72% similar
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