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Why would you shorten an embedding from 1536 dimensions to 256, and what do you give up by doing it?
Fewer dimensions means less storage per vector and faster similarity computation at query time, which matters directly at scale, a vector database holding millions of embeddings pays that storage and compute cost on every one of them. Modern embedding models are specifically trained to support this trade-off gracefully: a newer model shortened to 256 dimensions can still outperform an older, unshortened model at 1536 dimensions, so shortening isn't simply "less accurate," it's a deliberate trade against a specific model's own accuracy ceiling. What you give up is some of that ceiling, the shortened embedding is measurably less precise than the same model's full-length embedding, which is why the right dimension count depends on how much accuracy a specific use case can actually trade away.