Keeping training for 240 job roles current when the tools keep moving
Complete AI Training publishes courses, prompt libraries and tool guides for over 240 job roles. The hard part was never writing them. It was keeping them true.
A course that teaches a tool is out of date the week that tool ships a new version. Multiply that by 240 roles and every model, feature and interface they touch, and manual upkeep stops being possible. The old answer was to rewrite a course when somebody complained.
We built the upkeep into the platform. A watcher tracks the models, tools and products the material depends on. When one of them changes in a way that affects a lesson, a prompt or a screenshot, the affected material is pulled, redrafted against the change, and queued for a human editor. Nothing publishes without that editor pressing approve.
The same pipeline builds new role variants. A role is a set of tasks, so a new role is assembled from material that already exists, then edited rather than written from nothing.
What runs now: a dependency watcher over the models and tools the curriculum teaches, a redraft step that rewrites only what changed, an editor queue with approve and reject, and publication to the learning hub. Every change is logged against the lesson it touched, so it is always clear why a page moved.
What it taught us: the automation people actually want is not the one that writes the first draft. It is the one that notices the draft has gone stale.