How to Teach AI to Write Like You

If you have a ChatGPT or Claude subscription but have struggled to turn them into reliable workflows, this guide is for you.

Inside, you’ll learn:

✅ How to choose a first use case that is simple enough to control and valuable enough to produce a measurable result

✅ Which tasks current AI handles well and where it still struggles

✅ How to check whether a use case can fit into your existing systems and processes

✅ How to build an eval using representative business cases

✅ How to define success criteria, scoring rules, and critical failures before starting a pilot

✅ How to run the workflow in shadow mode before allowing it to affect real work

✅ Which metrics to track, including accuracy, corrections, review time, cost, and employee adoption

✅ How to decide whether to scale, adjust, or stop the workflow

This guide is specifically thought for people that want to implement AI in their own business, but you can use it also for your personal use cases.

I work with businesses implementing AI, and one of the most common mistakes I see is moving from a successful demo directly into a pilot. The workflow meets real company data, edge cases, permissions, and system constraints for the first time during the pilot. When it fails, the team has no agreed way to measure what went wrong.

With this guide, I am sharing the process I use to help companies select one workflow, design the eval, run a controlled pilot, and decide whether the result is reliable enough to scale.