Verified65% confidenceOpinionTime unknown
When deploying RAG in engineering, the quality bottleneck is usually not the model itself, but the design quality of the retrieval pipeline.
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65%
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5/31/2026
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UnverifiedThe difficulty of RAG lies not in getting the demo running but in retrieval accuracy; chunking granularity, embedding model selection, and recall strategy all affect the final result.78% similarUnverifiedThe quality bottleneck of a RAG pipeline often lies on the retrieval side rather than the generation side; if the recalled passages are semantically far from the question, the model will struggle to provide accurate answers.73% similarUnverifiedRAG管道中检索后的生成调用通常输入token量大但不需要极深推理，适合高吞吐低延迟的Flash类模型72% similarUnverifiedIn RAG practice, attention must be paid to chunking granularity, retrieval recall, and the strategy design of 'refusing to answer when retrieval fails.'70% similar
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