RAG systems get continuous evaluation loop for data-driven optimization

This article details the creation of a continuous evaluation loop for retrieval-augmented generation (RAG) systems, aiming to move beyond subjective improvements to data-driven optimization. It addresses three key challenges: the lack of a baseline for measuring changes, difficulty in pinpointing the source of errors, and the degradation of performance over time due to outdated evaluation sets. The solution involves establishing a fixed, human-annotated golden test set with 80 rules across Environmental, Social, and Governance categories for three industries, alongside layered metrics and a regression gate to ensure sustained performance. AI

IMPACT Establishes a framework for objectively measuring and improving RAG system performance, crucial for reliable AI deployments.

RANK_REASON Article details a methodology for improving RAG systems, including code snippets and a detailed explanation of a golden test set construction. [lever_c_demoted from research: ic=1 ai=1.0]

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• energy
• esg/routers/evaluation.py
• esg/services/evaluation_service.py
• financial services
• governance
• GPT-4
• industrial manufacturing
• production-rag-engineering
• retrieval-augmented generation
• Vietnam

• product
• infra

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