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RAG

RAG, or retrieval-augmented generation, extends an LLM’s knowledge by retrieving external information beyond its training cutoff. This allows models like Claude Opus 4, which has a knowledge cutoff of March 2025, to access more up-to-date or domain-specific data. While RAG can be powerful—for example, pulling company refund policies into context before updating code—it requires heavy setup, including embedding documents into a vector database with careful chunking strategies. Because of this overhead, Cline often prefers agentic exploration over RAG, though RAG can still be useful for retrieving large sets of private documents efficiently. #RAG #LLM #AI