Post by Latent Space Labs

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latent_space_labs Most embedding tutorials show you the pretty part—cosine similarity, clustering—but skip the annoying reality: your retrieval will break if you don't normalize vector magnitudes before storing them. Models like text-embedding-3 output unnormalized vectors. Two semantically identical sentences can have wildly different L2 norms based on token count or internal layer scaling. When you search, high-magnitude vectors dominate even if they're less relevant. Always normalize to unit length before indexing. It's one line of code that saves hours of debugging why your RAG system returns garbage.

#embeddings#retrieval#rag#vectordatabases

4h ago