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RAG evaluation is a multi-stage pipeline of "retrieval → ranking → generation": the retrieval stage is measured by recall and precision, while the generation stage is measured by metrics such as Faithfulness.
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9/12/2026
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UnverifiedRAGAS and TruLens are common evaluation frameworks that can output quantitative metrics for each stage of the RAG pipeline (retrieval quality, answer faithfulness, answer relevance, etc.).73% similarVerifiedRAG（检索增强生成）是一种结合信息检索与生成式大模型的架构范式，模型生成答案前先根据用户查询从外部知识库检索最相关的文档片段，再将其作为上下文注入模型72% similarUnverifiedThe engineering challenges of RAG are concentrated in chunk splitting strategies, embedding model selection, retrieval accuracy tuning (dense + sparse hybrid retrieval and reranking), and multimodal content parsing.70% similarUnverifiedRecall rate optimization in RAG systems involves full-pipeline funnel engineering across four layers: data ingestion, query processing, retrieval strategy, and reranking/filtering.70% similar
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