If embeddings are so powerful, why is retrieval their only mainstream use?
ProposalOrganic1043 · reddit · 2026-09-14
A Reddit post asks why embeddings remain confined to retrieval. OpenAI's original embedding release touted native capabilities across search, clustering, recommendations, anomaly detection, diversity measurement and classification, yet the ecosystem has funneled everything into vector DBs, RAG and now GraphRAG. The author notes the irony of widespread 'RAG is dead, ROI isn't worth it' complaints while teams ignore untapped value in embeddings they already store, speculating retrieval dominates simply because it's the easiest to productize.
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