Every AI feature we've shipped that didn't have evals on day one ended up needing them within a month — usually after a silent regression embarrassed someone. We now treat evals as a non-negotiable part of the first sprint, alongside the database schema and the deploy pipeline.
The reason is simple: LLM outputs change constantly. Models get updated, prompts get tuned, retrieval indexes drift, and your users find inputs you never imagined. Without a harness, you find out from a support ticket.
What 'evals on day one' actually means
We start with 20–50 hand-picked input/output pairs that represent the real job. Half are the obvious happy path. The other half are the weird, the malicious, and the edge cases we already know break things.
Each pair has a grader — sometimes a string match, sometimes a regex, often another model call with a strict rubric. The grader runs in CI on every PR that touches the prompt, model, or retrieval logic.
What you get
A green dashboard before you ship, and an alarm that fires the first time a model upgrade silently regresses a critical case. That's the whole pitch. The teams that adopt this stop having 'mysterious quality drops' six weeks in.