Unverified50% confidenceTradeoffExact time
Off-the-shelf systems encapsulate RAG details as a black box with no user intervention possible, making it difficult to investigate the cause when the agent gives a wrong answer—this is the most notable limitation compared to a self-built RAG workflow.
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50%
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Long-term
Relevance
9/18/2026
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UnverifiedTraditional RAG systems have three major limitations: returning empty results when the answer cannot be retrieved, inability to answer metadata questions, and lack of multi-turn iterative error correction.75% similarUnverifiedTraditional RAG systems can only passively invoke pre-loaded theoretical knowledge and cannot actively perform real operations.75% similarUnverifiedTraditional RAG systems tend to only accumulate documents without continuously assessing the timeliness of information.74% similarUnverifiedRetrieval-Augmented Generation (RAG) cannot completely eliminate hallucinations, as the model may still misinterpret retrieved documents or fill in gaps on its own when retrieval results are insufficient.74% similarVerifiedRetrieval-Augmented Generation (RAG) technology can partially mitigate hallucination issues by introducing external knowledge sources, but its effectiveness is limited in vulnerability analysis scenarios that require deep code understanding.74% similar
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