Translations:FACTS About Building Retrieval Augmented Generation-based Chatbots/32/zh: Difference between revisions

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'''RAGOps''': Effective health monitoring of RAG pipelines is essential once they are deployed. When answer quality is poor, a thorough error analysis is required to determine whether the issue lies in retrieval relevancy or LLM response generation. To debug retrieval relevancy, developers need detailed information on which chunks were stored in vector databases with their associated metadata, how queries were rephrased, which chunks were retrieved, and how those chunks were ranked. Similarly, if an LLM response is incorrect, it is crucial to review the final prompt used for answer generation. For issues with citations, developers must trace back to the original document links and their corresponding chunks. RAGOps/LLMOps and evaluation frameworks, such as Ragas, are critical for providing the necessary automation to enable rapid iteration during accuracy improvement cycles in RAG pipelines.

RAGOps：一旦 RAG 管道部署后，进行有效的健康监测至关重要。当答案质量较差时，需要进行彻底的错误分析，以确定问题是出在检索相关性还是 LLM 响应生成上。为了调试检索相关性，开发人员需要详细了解哪些数据块存储在向量数据库中及其相关元数据，查询是如何被重新措辞的，哪些数据块被检索到，以及这些数据块是如何被排序的。同样地，如果 LLM 响应不正确，审查用于生成答案的最终提示至关重要。对于引用问题，开发人员必须追溯到原始文档链接及其对应的数据块。RAGOps/LLMOps 和评估框架（如 Ragas）对于提供必要的自动化至关重要，以便在 RAG 管道的准确性改进周期中实现快速迭代。