Building Evals

Evaluation frameworks for AI systems, models, and agents.

Why evals matter

Evals measure whether a system behaves as intended: accuracy, safety, latency, and consistency. They help catch regressions and guide improvements.

What to evaluate

• Correctness — Does the output match the expected answer?
• Relevance — Does it address the question or task?
• Safety — Does it avoid harmful or inappropriate content?
• Latency — Does it respond in acceptable time?
• Consistency — Does it behave similarly across runs?

Evaluation approaches

• Human eval — Manual scoring; gold standard but expensive
• Automated — LLM-as-judge, heuristics, or rule-based checks
• A/B testing — Compare variants in production
• Regression suites — Fixed test sets to catch breakage

Building an eval suite

1. Define success criteria
2. Create a representative test set
3. Run evals regularly (CI or scheduled)
4. Track metrics over time and alert on regressions