Distinguish semantic similarity from factual verification when interpreting embeddings and vector search results.
Embedding search is good at finding what sounds related. It is not the same as proving what applies. Embeddings Map Similarity, Not Truth A MECE frame prevents a common AI search mistake: mixing categories that should stay separate. Similarity means two texts occupy nearby territory in vector space. Relevance means the retrieved text helps answer the user's current question. Truth means the final claim is supported by authoritative evidence. Those are connected, but they are not interchangeable. Embeddings work because language has patterns. Texts about refund windows, warranty windows, and trial windows share words and concepts, so their vectors may…
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