Prompt Evaluation & Testing

Testing prompts helps you know when a change makes things better or worse.

Ways to test (easy):

• Unit tests: For a set of sample inputs, check that the output matches a pattern or schema (e.g., JSON keys present, length limits).
• A/B tests: Send two prompt variants to users (or synthetic inputs) and compare which performs better on your metrics.
• Human evaluation: Ask people to rate outputs for usefulness, clarity, and safety.

Useful metrics to track:

• Format compliance rate (how often the model returns valid JSON, CSV, etc.)
• Task accuracy (correctness of facts or labels)
• Human preference score (how often humans prefer variant A over B)
• Failure modes and error reasons (parsing errors, hallucinations)

Quick test harness idea:

1. Keep a small set of representative inputs (train/dev/test style).
2. For each template change, run the prompts against the inputs and record outputs.
3. Apply automatic checks (regex, JSON schema) and store pass/fail counts.
4. Complement with periodic human reviews on a sample.

Monitoring in production:

• Log samples and failures (redact PII) and alert when error rates increase.
• Track metrics over time to detect drift after model or template updates.

Automating these steps helps you move quickly while keeping quality safe.