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-5, 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.

Building an Evaluation Dataset

The foundation of any LLM evaluation is a high-quality test set. Aim for 100–500 examples that cover your main use cases, edge cases, and failure modes. Include both “easy” cases (should always pass) and “hard” cases (known failure modes). Label each example with the expected output or at least the expected behavior.

Key Metrics by Task Type

  • Q&A / RAG: Exact match, F1 score, and LLM-as-judge (use GPT-5 to grade responses on a 1–5 scale for relevance, accuracy, and completeness).
  • Summarization: ROUGE-L for overlap, plus LLM-as-judge for coherence and faithfulness (does the summary contain information not in the source?).
  • Classification: Precision, recall, F1 per class. Also track the confusion matrix to find systematic misclassifications.
  • Code generation: Pass@k (does the code pass unit tests?), plus human review for style and maintainability.

Automation and Regression Testing

Treat LLM evaluation like unit tests. Run your eval suite on every prompt change, model update, or configuration tweak. Tools like LangSmith, Braintrust, or custom scripts with pytest can automate this. Set a quality gate: if any metric drops by more than 5% compared to the baseline, block the change and investigate.