Translations:FACTS About Building Retrieval Augmented Generation-based Chatbots/32/en

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.