Embeddings Are Similarity, Not Truth
Explain what embeddings capture and why similarity scores still need task-specific evaluation.
Reframe: embeddings are a map of learned closeness, not a judge. The Mechanism An embedding model turns words, sentences, or passages into vectors. Similar texts often sit near each other, so cosine similarity can retrieve paraphrases that exact keyword matching misses. This is why embeddings are useful for semantic search and duplicate detection. The Boundary Similarity is trained from patterns, not from your team's definition of relevance. A passage can be semantically close and still be stale, unauthoritative, too broad, or unsafe. Professional use means measuring retrieval against labeled examples from the actual job. The Practical Pattern Use embeddings to…
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