Eval (Evaluation)

Evals are how you know your AI features work — not just sometimes, but reliably and consistently.

Why Evals Matter

Without evals:

• "It seems to work when I test it manually"
• No way to measure improvement or regression
• Users discover failures in production
• No data to support decisions about models or prompts

With evals:

• Measurable quality metrics
• Catch regressions before deployment
• Data-driven model and prompt selection
• Confidence in shipping AI features

Types of Evals

Building Simple Evals

1. Define success — What does "correct" look like?
2. Create test cases — Input-output pairs with known answers
3. Run the eval — Feed inputs through your AI feature
4. Score results — Compare outputs to expected answers
5. Iterate — Improve prompts or switch models based on scores

Eval Metrics

• Accuracy — % of correct responses
• Precision — Of items flagged positive, how many were actually positive?
• Recall — Of actual positives, how many did AI find?
• F1 Score — Balance between precision and recall

For Vibe Coders

If your product uses AI, start with basic evals:

• Does the AI feature work for your 10 most common use cases?
• Does it handle edge cases gracefully?
• Is output quality consistent across runs?

Simple evals catch big problems. Start basic and improve over time.