2 posts tagged with "prompts"

Walk through any mature engineering team's PR queue and you will see the same thing: a four-line bug fix attracts three rounds of comments about naming, error handling, and missing test coverage, while a forty-line edit to the system prompt sails through with a single "LGTM, ship it." The author shrugged because the diff looks like documentation. The reviewer shrugged because they have no mental model of what "good" looks like inside that block of English. The result is a prompt change with the blast radius of a feature launch, reviewed at the bar of a typo fix.

This is the quiet quality crisis of every team building with LLMs in production. The codebase has decades of accumulated discipline — linters, type checks, code owners, test gates, deploy windows. The artifacts that actually steer the model — the system prompt, the eval rubric, the tool description, the few-shot exemplars — sit in the same repo and ship through a review process that was designed for English prose. So prompt regressions, eval-rubric drift, and tool-schema breakages land at a quality bar the team would never accept for code.

Ask any AI engineering team if they test their prompts and they'll say yes. Ask if a bad prompt can fail a pull request and block a merge, and you'll get a much quieter room. The honest answer for most teams is no — they have eval notebooks they run occasionally, maybe a shared Notion doc of known prompt quirks, and a vague sense that things are worse than they used to be. That is not testing. That is hoping.

The gap exists because prompt testing feels qualitatively different from unit testing. Code either behaves correctly or it doesn't. Prompts produce outputs on a spectrum, outputs are non-deterministic, and running enough examples to feel confident costs real money. Those are real constraints. None of them are insurmountable. Teams that have built prompt CI that actually blocks merges are not spending fifty dollars a build — they're running in under three minutes at under a dollar using a few design decisions that make the problem tractable.