LLM evaluation metrics extend traditional pass/fail testing with multi-dimensional scoring frameworks covering relevance, factual accuracy, hallucination rates, task completion, and safety. The post covers why traditional software metrics fail for probabilistic AI outputs, the main metric categories (correctness, hallucination/faithfulness, RAG-specific, agent task completion, safety), and how they are calculated via statistical methods (BLEU, ROUGE), model-based approaches (BLEURT, NLI, embeddings), and LLM-as-a-judge (G-Eval). It also surveys key tooling — Promptfoo for prompt regression testing, RAGAS for RAG evaluation, Langfuse for production observability, and LangSmith for debugging — and outlines a layered evaluation strategy integrating CI/CD quality gates, continuous production monitoring, golden datasets, and periodic human calibration.
Table of contents
1. What are LLM evaluation metrics?2. Why traditional software metrics don't work for LLMs3. What makes a good LLM evaluation metric?4. The main categories of LLM evaluation metrics5. How LLM evaluation metrics are calculated6. LLM evaluation frameworks and tools: how teams operationalize LLM evaluation in production7. Why human evaluation is important8. Common challenges in LLM evaluation9. Building an effective LLM evaluation strategyEvaluation is the foundation of AI qualityFAQSort: