How to Evaluate LLM Applications: Metrics, Tools, and Best Practices

Why Evaluation Matters

Unlike traditional software, LLM applications have non-deterministic outputs. You can't just write unit tests — you need systematic evaluation.

Key Metrics

• Accuracy: Does the output match the expected answer?
• Relevance: Is the response on-topic and useful?
• Faithfulness: Does the answer stick to the provided context?
• Toxicity: Does the output contain harmful content?
• Latency: How long does generation take?
• Cost: What's the per-request cost?

Evaluation Approaches

• Human evaluation: Gold standard but slow and expensive.
• LLM-as-judge: Use a strong model (GPT-4, Claude) to grade outputs.
• Automated metrics: BLEU, ROUGE for summarization; pass@k for code.
• A/B testing: Compare two versions with real users.

Tools

• RAGAS: Evaluates RAG pipelines (faithfulness, relevance, recall).
• DeepEval: Open-source LLM evaluation framework with 14+ metrics.
• LangSmith: Tracing and evaluation platform by LangChain.
• Braintrust: Evaluation and observability for AI products.

Best Practices

• Build an eval dataset of 100-500 examples covering edge cases.
• Run evaluations on every prompt change.
• Track metrics over time in a dashboard.
• Set quality gates: block deploys if scores drop below threshold.