Teaching Practical AI: Building a Custom GPT to Fix Documentation Chaos

This week, I helped a colleague tackle a familiar headache. Their documentation was scattered across formats, styles, and half-filled templates. Everyone had created own version, and every revision meant hours of reformatting. I showed them how to create a custom GPT that could collect and structure the information automatically for every new document that needed to be created.

We built it step by step. First, we defined what “good documentation” meant for their team. Then, we created a prompt framework that guided the GPT to ask for information in a clear, consistent way. Each response fed into a standard structure that produced a clean draft for review. The human stayed in control, approving and refining the content before publishing.

The result was more than time saved. The team gained a repeatable process that ensured accuracy and consistency without removing human oversight. Now every question needed to produce publishable documentation was being answered, in the same order, with the same level of detail. The GPT became a collaborator, handling the structure and input while people focused on communicating the necessary information.

This experience reinforced an important lesson. The most useful AI applications start small, with one clear pain point and a cheap, fast, reliable custom solution built around it. Teaching people how to frame prompts and design their own GPTs gives them independence. It turns AI from a black box into a practical tool they can shape to fit their work.