How to stop fighting with coherence and start writing context-generic trait impls

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The embedding retriever works differently from BM25 at every step. Instead of counting tokens, it converts each chunk into a dense numerical vector — a list of 1,536 numbers — using OpenAI’s text-embedding-3-small model. Each number represents a dimension in semantic space, and chunks that mean similar things end up with vectors that point in similar directions, regardless of the words they use.

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