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.
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:
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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