Evaluating Retrieval-Augmented Generation (RAG): Everything You Should Know

Retrieval Augmented Generation (RAG) is a widely adopted approach to enhance Generative AI applications powered by Large Language Models (LLMs). By integrating external knowledge sources, RAG improves the model's ability to provide accurate and contextually relevant responses. Despite its potential, RAG-generated answers are not always entirely accurate or consistent with the retrieved knowledge. In a recent webinar, Stefan Webb, Developer Advocate at Zilliz, explored evaluation strategies for RAG applications, focusing on methods to assess the performance of LLMs and addressing current challenges and limitations in the field. The talk covered various RAG pipeline architectures, retrieval and evaluation frameworks, and examples of biases and failures in LLMs. RAG architecture includes semantic search, which leverages vector databases for efficient searching over unstructured data to retrieve semantically similar contexts relevant to a user's query. A modular approach to building the RAG pipeline enables incremental improvements at each stage, addressing specific challenges and enhancing the quality of generated outputs. Evaluating foundation models requires a nuanced approach, as different aspects of the pipeline need to be evaluated. Performance evaluation includes task-based evaluation (using standard benchmarks) and self-evaluation (focusing on internal measures or introspection). Introspection-based evaluation can be divided into generation-based evaluation and retrieval-based evaluation, with relevant metrics such as faithfulness, answer relevancy, context relevance, and context recall. Challenges and limitations of LLM-as-a-Judge include position bias, verbosity bias, wrong judgments, and wrong judgment with chain-of-thought reasoning. Open-source evaluation frameworks like RAGAS, DeepEval, ARES, and HuggingFace Lighteval provide structured methodologies and tools to evaluate retrieval and generation performance effectively. The future of RAG lies in its adaptability and continuous refinement. Addressing current limitations and embracing innovative evaluation methods will be essential for unlocking the full potential of AI applications.