Unlock RAG Success: A New Debugging Framework for MLflow Users

This article introduces an innovative approach to debugging and understanding failures in Retrieval-Augmented Generation (RAG) systems. By moving beyond the vague term "hallucination," the author provides a practical, actionable framework for identifying and resolving specific RAG pipeline issues. This is a significant step towards improving the reliability and performance of Generative AI applications.