Start with one useful task.

You bring knowledge of your work. This path helps you use that knowledge to ask better questions, guide AI, and review the output.

Generative AI produces responses such as text, summaries, and draft analyses from your instructions and the information you provide. Its output can sound convincing even when something is missing or incorrect. Learning to check it is part of learning to use it.

Your beginner path

1. Complete your first useful workflow

   Choose a small business task, define the result, and work through an example. Start with the practice inputs so you can focus on the method.

   Try your first AI workflow

2. Give AI the context it needs

   Explain the situation, provide the relevant inputs, and describe the output you want. Compare a vague instruction with a prompt built around the actual task.

   Learn to give useful context

3. Check the response before using it

   Look for unsupported claims, missing information, and calculations that need checking. Learn how to turn an initial answer into work you can explain.

   Learn to verify an analysis

Choose your next exercise

• Finance: Draft a budget variance commentary from a practice workbook.
• Operations: Turn process notes into a clear procedure.
• Marketing: Find recurring themes in customer feedback.
• Data: Prepare a messy spreadsheet for analysis.

What you need

Each guide explains the tools, access requirements, inputs, and skills needed. Begin with the no-code exercises; explore low-code or Python guides when their prerequisites fit your goals.

Use the supplied examples before adapting a workflow to workplace information. Follow your organization’s rules when choosing tools or sharing data.

Keep your first goal small

Aim to produce one useful, checked result. Save what worked and note what you changed. That gives you a method to build on next time.