From Prompts to RAG to RAGAs: Evaluating Retrieval-Augmented Generation Systems the Right Way

Author(s): Edgar Bermudez

Originally published on Towards AI.

From Prompts to RAG to RAGAs: Evaluating Retrieval-Augmented Generation Systems the Right Way

Most RAG demos look impressive. But ask them the wrong question and they hallucinate, miss relevant docs, or contradict their own sources. RAGAs give AI engineers a way to measure and fix these failures before they hit production.

The article discusses the transition from traditional prompt engineering to Retrieval-Augmented Generation (RAG), highlighting the importance of using external documents to improve the performance of AI models by grounding answers in real data. It emphasizes the challenges of knowledge staleness and hallucinations in AI, presenting RAGAs as a solution for effectively evaluating RAG systems based on key metrics like retrieval quality and answer correctness. The article outlines a structured approach to system evaluation, offering a practical template for evaluating RAG pipelines to ensure they are reliable and effective in real-world applications.

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Published via Towards AI