Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, embedding them into a ...
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Beyond RAG as a buzzword: Aligning AI retrieval with real-world use
RAG talk is prevalent, yet simply stating “we used RAG” fails to provide much information about its effectiveness in a production environment. A more effective approach is to examine how you align ...
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