Why Every Product Team Needs an AI Evals Practice (and How to Start)

The quality problem with AI features

Traditional software either works or it doesn't. AI features exist on a spectrum — they're sometimes right, sometimes wrong, and the failure modes are often subtle. Shipping AI without evals is like shipping code without tests: it works until it doesn't, and you find out at the worst possible moment.

What evals actually are

An eval is a structured test that measures whether your AI system's output meets a defined quality criterion. That criterion might be:

• Factual accuracy: Does the output match the source document?
• Format compliance: Does the output match the required schema?
• Tone and brand voice: Does the output sound like your brand?
• Task completion: Did the agent actually accomplish the goal?
• Safety: Did the output avoid prohibited content?

The simplest eval you can ship today

Start with a golden dataset: 20–50 input/expected-output pairs that represent the core use cases your AI feature handles. Run your system against them. Score each output against the expected result — manually first, then with Claude as a judge once you have enough volume. Track the score over time. That's an evals practice.

Scaling from golden sets to continuous evals

As your AI features mature, evals evolve:

1. Unit evals: Golden datasets for specific capabilities
2. Integration evals: End-to-end workflow tests across your full stack
3. Production monitoring: Sampling live outputs and scoring them continuously
4. Regression detection: Alerts when score drops after a model or prompt change

Tooling

For most product teams, the right starting point is a simple Python script + spreadsheet. When you outgrow that: Braintrust, LangSmith, or a custom pipeline on top of the Claude API work well. The key is instrumenting your system before you need the data, not after.