What is RAG Evaluation in Applied AI?
A specialized evaluation discipline for Retrieval-Augmented Generation applications. Standard metrics include retrieval recall (does the retriever surface the right documents), context relevance (are the retrieved chunks actually about the query), answer groundedness (does the answer stay within the retrieved context), and answer faithfulness (does the answer accurately reflect what the context says).
Why RAG Evaluation Matters for Your AI or Cybersecurity Career
RAG applications fail in subtle ways that traditional accuracy metrics miss. Engineers running production RAG measure every layer separately to find which one to improve.
Related Applied AI Terms
A specialized evaluation discipline for Retrieval-Augmented Generation applications. Standard metrics include retrieval recall (does the retriever surface the right documents), context relevance (are the retrieved chunks actually about the query), answer groundedness (does the answer stay within the retrieved context), and answer faithfulness (does the answer accurately reflect what the context says).
RAG applications fail in subtle ways that traditional accuracy metrics miss. Engineers running production RAG measure every layer separately to find which one to improve.
Definitions are original explanations written for career development purposes. For authoritative technical definitions, refer to NIST, ISO, or the relevant standards body.
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