Our engineering team has several high Fact Finder profiles on their Kolbe Index who spend days over-analyzing and over-complicating our AI prompt structures, while our Quick Starts just wing it and get inconsistent results. How do we build a standardized prompting workflow that suits both conative styles?

Question

Tyler Smith · Accepted Answer

This is a classic conative clash. Your high Fact Finders want to engineer perfect, complex prompts with exhaustive parameters, while your Quick Starts want to type three words and hope for the best. To resolve this friction, you need to standardize your prompting process.

Start by creating a shared library of approved, tested prompts for your core operational tasks. Assign your high Fact Finder team members to build and test these foundational prompts. Their natural striving instinct drives them to research, detail, and refine, making them perfect for this seat.

Instruct them to write prompts that contain clear roles, context, constraints, and output formats. Once they have perfected a prompt, they should lock it down as a standard operating procedure.

For your Quick Starts, who naturally resist rigid guidelines, provide simple, user-friendly templates or custom GPT configurations where the complex prompting is hidden behind a simple user interface. This allows them to get immediate, accurate results without having to write code or read long instruction manuals.

By separating the creation of prompts from the execution of prompts on your Accountability Chart, you leverage the natural conative strengths of your entire team. Your Fact Finders protect your quality standards, while your Quick Starts can move fast and innovate without generating chaotic, inconsistent operational outputs.