Module 4 of 4Lesson 1 of 4~10 min
Turn a good result into a reusable workflow
A request that worked well once deserves to be kept and shared. This lesson shows you how to save it durably in your tool, then document it as a workflow with the points where a person needs to check the result. Your team can then reuse it without starting from scratch.
Lesson objective
By the end of this lesson, you will be able to save a proven request as standing context (project, instructions, shared template) and document it as a workflow with its human checkpoints.
Topics covered
- AI workflow
- ChatGPT and Claude projects
- custom instructions
- knowledge capture
Where it fits
Capture it and share it with the team
How do I turn a good result into a reusable, shared method without losing control?
Lessons in this module
- Turn a good result into a reusable workflow (this lesson)
- Move to an online deliverable without mistaking a prototype for a product
- Write your team's AI policy
- Your 30-day plan and resources
What you will learn in the course
This lesson is part of the course Put AI to work in your business
- Explain, on a concrete case, why an AI assistant makes things up, forgets or misses recent information, and decide how to use it accordingly.
- Sort data before giving it to an AI tool (allowed, anonymize first, forbidden) and configure the tool to match.
- Choose which tasks to hand to AI and how to work with it (automate, assist, delegate) based on value, risk and the data involved.
- Write a complete request (task, context, examples, format, constraints) and improve it over at least two documented iterations.
- Pick the right tool for a deliverable and justify the choice on data, cost, licensing and integration.
- Check AI output against an explicit checklist (facts, figures, sources, tone, compliance) before using it.
- Turn a successful use into a reusable workflow with human checkpoints, and set the team's rules of use in an AI policy.
Related courses
- Understand what LLMs do well, and where they failAll levels · ~2 hr
- Choosing and using AI tools at workAll levels · ~1 hr 40 min
- Prompting for product managers: delegate, describe, verifyJunior · ~2 hr 45 min