Think of embeddings as meaning you can measure

The reframe: an embedding is a position, not an answer. Embeddings are useful because computers compare numbers more directly than they compare meaning. A text embedding turns a sentence, paragraph, product, ticket, or image into a vector: a list of floating-point values. Once two items are vectors, the system can measure how close they are. Close usually means related; far usually means unrelated. This is why embeddings power semantic search, clustering, recommendations, anomaly detection, and classification. The same basic move appears in each use case: convert messy material into comparable vectors, then use distance to find neighbors or groups. The…

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