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Latent Space · February 15, 2025 · 70m
The State of RAG: What Works, What Does Not
A practitioner's guide to Retrieval Augmented Generation in 2025: which patterns work in production, which are overhyped, and where the technology is headed.
This summary was generated from show notes and public descriptions, not from a full transcript review. Details may contain inaccuracies.
Canon
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RAG is fundamentally about enriching the model's environment with relevant context at inference time, providing the information environment that shapes better outputs.
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RAG practitioners can control retrieval quality (indexing, chunking, ranking) but cannot fully control generation quality. The Stoic approach focuses on retrieval excellence.
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