Evaluating LLM and RAG Systems
Focusing on key Metrics There is no single metric that can fully capture the functionality of Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) systems. In this document, we discuss the metrics that are particularly important when evaluating LLM and RAG systems, both the atomic components and the holistic system. Key Metrics for RAG Evaluation: 1. Faithfulness Definition: Measures the factual consistency of the generated answer against the given context. Break the generated answer into individual statements. Verify if each statement can be inferred from the given context. Scaled to a range of 0 to 1, with higher scores indicating better faithfulness. 2. Answer Relevance Definition: Assesses how pertinent or relevant the generated answer is to the given prompt. Generate multiple variants of the question from the generated answer using an LLM. Measure the mean cosine similarity between these generated questions and the original question. Higher scores indicate b...