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In RAG design, the scale of retrieval results must be controlled: stuffing in too many documents drives up token costs and dilutes key information; and retrieval must occur within the scope of data the user has permission to access to avoid indirect leakage of unauthorized data.
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10/13/2026
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UnverifiedThe advantage of RAG architecture is that new knowledge can be injected without retraining the model, but the trade-off is that prompt length (number of input tokens) grows significantly as retrieved content increases.75% similarUnverifiedThe effectiveness of RAG is constrained by three stages: document chunking and vectorization quality, vector retrieval recall rate, and the timeliness of knowledge base data.75% similarUnverifiedNotebookLM's RAG implementation strictly limits the retrieval scope to the dataset actively uploaded by the user, significantly improving retrieval accuracy and citation traceability compared to general RAG systems.74% similarUnverifiedRAG 系统中召回太多会浪费上下文窗口空间，召回太少则可能遗漏关键信息，检索精确度是关键挑战74% similar
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