Unverified50% confidenceFactExact time
In AI FAQ scenarios, RAG is usually divided into two phases, offline and online: the offline phase splits FAQ documents into chunks, converts them into vectors via an embedding model, and stores them in a vector database; the online phase vectorizes the user question, recalls the top-k passages using cosine similarity, and then hands them to the LLM to synthesize an answer
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50%
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Long-term
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10/15/2026
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VerifiedIn the offline phase of the traditional RAG process, documents are chunked and converted into vectors via an embedding model and stored in a vector database; in the online phase, similar document chunks are retrieved using an Approximate Nearest Neighbor (ANN) algorithm.77% similarVerifiedRetrieval-Augmented Generation (RAG) splits codebases into semantic segments via vector databases and dynamically retrieves the most relevant content to inject into the context when an AI handles specific tasks.76% similarVerifiedThe industry typically employs a RAG architecture to address the long-term memory problem in AI companions, storing historical conversations as vectors in a local database and dynamically retrieving them to inject into prompts.72% similarUnverifiedA typical RAG system contains two stages: the data preparation stage and the question-answering generation stage.71% similarUnverifiedThe Embedding model appears twice in the RAG pipeline: during offline knowledge base construction and during online retrieval, with the goal in both cases being to map text into the same vector space.70% similar
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