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7/14/2026
The original title is about RAG evaluation with RAGAs metrics. Let me rewrite it to be punchy and specific while preserving key facts.

The original title is about RAG evaluation with RAGAs metrics. Let me rewrite it to be punchy and specific while preserving key facts.

Original: RAG Evaluation with RAGAs: Faithfulness, Context Recall, and Answer Relevance

Short summary

A real-world case study of a Vietnamese bank's compliance assistant hallucinating regulatory facts despite correct retrieval, illustrating why RAGAs evaluation is essential. The article explains faithfulness, context recall, and answer relevance as independent diagnostic metrics for each RAG pipeline layer, with code examples and a practical LLM-as-judge setup costing under $5 per weekly eval run. It recommends shipping a real-time faithfulness gate before launch to block confident wrong answers.

  • •RAGAs decomposes RAG quality into per-layer metrics: faithfulness, context recall, answer relevance
  • •A bank's compliance assistant hallucinated penalty rules for a month before a domain expert caught it
  • •LLM-as-judge with gpt-4o-mini costs under $5 per 100-question weekly eval; add a faithfulness gate before launch

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