LLM Evaluation Metrics in Python: Complete Guide with RAGAS and DeepEval

LLM evaluation is the most underrated part of AI engineering in 2026. Everyone focuses on building pipelines, but nobody measures whether the output is good. ## The 4 Essential RAG Metrics ### 1. Faithfulness Does the answer stay true to the retrieved context? This is the most important metric. Using RAGAS: ```python from ragas.metrics import faithfulness result = evaluate(dataset=your_dataset, metrics=[faithfulness]) ``` ### 2. Context Relevancy Are the retrieved documents relevant to the question? ```python from ragas.metrics import context_relevancy result = evaluate(dataset=your_dataset, metrics=[context_relevancy]) ``` ### 3. Answer Correctness Does the answer match the ground truth? Requires a reference answer. ### 4. Hallucination Rate Percentage of answers containing info not in provided context. ## Using DeepEval for Production DeepEval (16,885 stars) supports 14+ metrics and CI/CD integration. ```python from deepeval import evaluate from deepeval.metrics import AnswerRelevancyMetric, FaithfulnessMetric ``` ## Which Metrics to Track | Metric | Tool | Target | |--------|------|--------| | Faithfulness | RAGAS | >0.85 | | Context Relevancy | RAGAS | >0.70 | | Answer Relevancy | DeepEval | >0.80 | | Hallucination Rate | DeepEval | <0.10 | | Latency | Custom | <2s | ## The Bottom Line Ship your RAG app with at least faithfulness + context relevancy monitoring. Add more metrics as you scale.