RAG Evaluation Metrics: Answer Relevancy, Faithfulness, and Real-World Accuracy

Retrieval-Augmented Generation (RAG) represents a significant advancement in natural language processing by integrating large language models with external knowledge retrieval, allowing it to access current information and mitigate factual inaccuracies inherent in static generative models. This hybrid approach necessitates novel evaluation metrics that surpass traditional measures like perplexity or BLEU, which do not adequately address the quality of retrieved context or model fidelity to it. Key evaluation metrics for RAG include retrieval precision and contextual relevance, which ensure the relevance and semantic alignment of retrieved documents; answer relevancy, which evaluates the coherence of generated responses with the query; and faithfulness, which assesses the adherence of outputs to their source documents to reduce hallucinations and enhance reliability. Real-world accuracy metrics are also crucial, testing RAG systems' performance against domain-specific data and tasks, ensuring practical applicability. Together, these metrics form a comprehensive framework that supports the development of reliable and trustworthy RAG systems, critical for applications in high-stakes fields such as healthcare, law, and finance.