Shipping evals before prompts

Evals are the new unit tests

When a product team ships an LLM feature, the temptation is to iterate on prompts in production. That works until a model update silently changes tone, drops a compliance guardrail, or hallucinates a price.

We treat eval suites like any other regression gate: fixed inputs, expected properties, and a fail-fast CI step before deploy.

What we actually measure

Not every output needs to match word-for-word. We score:

• Structural checks — JSON shape, required fields, max length
• Semantic rubrics — a smaller model or rule set flags policy violations
• Golden-set similarity — embedding distance against approved answers for high-risk flows

Practical rollout

Start with ten cases from real support tickets or sales calls. Add one new failure every time production surprises you. Within a month you have a meaningful safety net without blocking experimentation.

When to skip

Exploratory prototypes and internal tools can move faster. Customer-facing flows with money, health, or legal implications cannot.

Start a project if you want help standing up evals for your next AI release.