Unverified50% confidenceFactExact time
The RAG (Retrieval-Augmented Generation) pipeline introduces a new failure point: when the retrieval step pulls the wrong documents, the LLM will still write a fluent but confident and wrong answer based on the wrong documents, so hallucinations may start at the retrieval stage rather than in the model itself.
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50%
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Long-term
Relevance
10/14/2026
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VerifiedRAG (Retrieval-Augmented Generation) retrieves relevant document chunks from a vector database and injects them into the prompt before calling the LLM; when the retrieved documents are mismatched or contain incorrect information, 'retrieval-driven hallucinations' can occur.79% similarUnverifiedIn data pipeline debugging scenarios, LLM hallucinations directly manifest as incorrect root cause identification, potentially misleading engineers in their troubleshooting.76% similarUnverifiedLimitations of RAG include that poor quality of retrieved documents, retrieval failures, and the model ignoring retrieved evidence (attention drift) can still lead to hallucinations.76% similarUnverifiedChunk boundaries in RAG pipelines are often wrong, as fixed-size chunking strategies ignore cross-paragraph information dependencies within documents, causing retrieved segments to lack context and leading to hallucinations.75% similarUnverifiedRAG 并非万能药，当检索到的原始文档质量低劣、语义匹配不精准或模型整合时过度发挥，幻觉依然会出现75% similar
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