Part 6 — RAG Recall Quality from 60% to 93%: Building a Continuous Evaluation Loop (Not Gut Feeling)
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measuring changes, difficulty in pinpointing the source of errors, and the degradation of performance over time due to outdated evaluation sets.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.