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Most internal AI tools are shipped, celebrated, and then quietly left to degrade. This episode breaks down how to build a real evaluation practice — so you always know whether your tool is still doing its job.

Show Notes

Shipping an internal AI tool is the easy part. Knowing whether it's still working six months later — that's where most teams go silent. This episode of Development tackles the "eval gap": the absence of any repeatable system to detect when an AI tool's output quality has silently drifted, and what it actually takes to close it.

The episode walks through a concrete, step-by-step framework for building an evaluation practice around custom internal tools — from structured data extraction to open-ended response drafting. Key topics covered include:

  • Why silent degradation happens — model provider updates, shifting inputs, and accidental prompt edits can all erode output quality without triggering any error or alert.
  • Building a golden set — how to curate 20–30 representative historical examples with verified correct outputs, and why this investment pays off more than any other part of the process.
  • Defining a rubric — the difference between binary scoring for structured tasks and dimension-based scoring for open-ended outputs, and how to make either one fast enough to actually run.
  • Using a model as a critic — why prompting an AI to answer specific yes-or-no questions about another AI's output is a reliable evaluation method, and what makes it work.
  • Setting thresholds with consequences — how to turn evaluation scores into operational decisions, so results on a reporting dashboard drive real action rather than sitting unread.
  • The three failure modes — stale golden sets, evaluations too slow to run consistently, and waiting until something breaks before building the practice at all.

The episode makes a strong case that evaluation isn't a one-time audit or a technical luxury — it's the operational layer that separates a tool that holds up over time from one that quietly becomes a liability. The earlier it's built into the workflow, the more useful it becomes as part of a broader business operating system. If you enjoyed this one, the episode How to Run a Debrief So It Actually Feeds Your Next Bid applies a similar operational lens to a different part of the business cycle.

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What is DEV?

Software and web development from the side that has to ship it and then live with it. Architecture decisions with a cost attached, scoping, technical debt, hiring and vendor selection, and the AI tooling question every engineering team is now answering whether they planned to or not.

Each episode takes one decision — rewrite or refactor, framework choice, build versus buy, how to scope a fixed-bid project honestly — and works through the tradeoffs, including the ones that only show up in year two. Written for engineering leads, technical founders and the people who fund them. Five or six minutes, no hand-waving.

Topics include rewrite versus refactor, build versus buy, scoping fixed-bid work honestly, technical debt you should keep, framework and platform choices, hiring and vendor selection, code review culture, and where AI tooling actually helps.

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