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In the RAG retrieval process, the user's question is first passed to an Embedding model to be converted into a vector, then similarity calculations are performed in a vector database to retrieve the top-K most relevant documents, which are then concatenated with the original question and passed to the large model to generate an answer.
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10/10/2026
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UnverifiedThe Naive RAG workflow is: convert the user query into a vector, perform a single vector similarity search, retrieve the most relevant chunks, and then feed the context together with the original question into an LLM to generate an answer.76% similarVerifiedRAG converts user questions into vectors during the retrieval phase to recall document fragments, then injects the recalled content into the prompt during the generation phase, thereby reducing model hallucination.75% similarVerifiedThe RAG mechanism retrieves semantically similar business data fragments from a vector database and injects them into LLM prompts, addressing LLM knowledge cutoff and hallucination issues without requiring model retraining74% similarUnverifiedThe RAG pipeline involves multiple stages including document chunking strategy, vectorization method, retrieval algorithm, re-ranking, and prompt templates; a problem in any single stage will affect the quality of the final answer.73% similar
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