An Accounting Department is thrilled with the RAG Application that the Application & Data Science...

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According to Nutanix Enterprise AI architecture recommendations for improving Retrieval-Augmented Generation (RAG) performance, when the issue is specifically related to the relevance of the retrieved documents (context) rather than the generation capability of the LLM, the standard solution is to introduce arerankingstep.

Problem Identification:The user explicitly states that when therightdocuments are used, the output is good, but 20% of the time the retrieval is generic or irrelevant. This indicates a precision issue in the retrieval phase, not a generation issue.

Solution (Reranking):Arerank model(or Cross-Encoder) is deployed as a post-retrieval step. It takes the top N documents retrieved by the initial embedding search and re-scores them based on their direct relevance to the specific query, re-ordering the list to ensure the most pertinent information is passed to the LLM. This improves precision without requiring the expensive and time-consuming process of replacing the embedding model or re-indexing the entire database.