Retrieval-Augmented Generation (RAG) systems are powerful because they use external knowledge. But they only work well if the retrieved information is relevant and the model uses it correctly. RAG evaluation focuses on testing three core areas: retrieval accuracy, groundedness, and completeness.
By evaluating how the system handles different types of user queries clean, messy, ambiguous, and incomplete you can clearly see where responses break down. This helps fine-tune both retrieval and generation steps, making answers more trustworthy and useful.
Reliable RAG systems need continuous evaluation, not one-time testing.
Further Reading:
https://github.com/future-agi/ai-evaluation
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