Evaluating GEN AI Systems Beyond Traditional Metrics

This document is a guide that discusses the evaluation of GEN AI systems, highlighting the limitations of traditional metrics in measuring their performance. It explains how traditional evaluation methods, which worked for older question-answering systems, fail to accurately assess GEN AI systems that synthesize information rather than merely extracting it. The guide outlines the hallucination problem, where systems may provide incorrect answers that appear accurate due to flawed metrics. It presents better evaluation methods, such as component-based evaluation and multi-dimensional quality assessment, which consider various aspects of the responses, including accuracy, relevance, coherence, and completeness. Additionally, it emphasizes the importance of trust and reliability in GEN AI systems, particularly in critical fields like healthcare and legal advice. The document concludes with practical steps for improving evaluation processes, including the integration of human feedback and continuous monitoring of system performance.