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Prompting and AI Workflow Design

Classroom Explanation

In this lesson, treat 07.03 Ask for critique before revision as a work-design decision. The goal is not to find a magic phrase. The goal is to make the task clear enough that you can judge the result. A strong prompt workflow says what job is being done, what context is safe to share, what output format is useful, and how a person will review the result before using it.

Think of this as a classroom habit. First, name the work in plain English. Then name the evidence you need. Then name the action or decision the result will support. This keeps the lesson grounded. It also prevents the common mistake of letting a tool, chart, model, or AI output look more certain than it really is.

Step-by-Step

  1. Say the purpose of this step in one sentence.
  2. Name the input: data, context, examples, files, or assumptions.
  3. Name the output: table, chart, model result, prompt, report section, or decision note.
  4. Add one check that would catch a weak or unsafe result.
  5. Write one limitation before moving to the next step.

Practical Example

Imagine you are working on Iteration and Follow-Up Questions. The beginner mistake is to jump straight into the tool. A better move is to pause and ask: What exactly am I trying to learn or produce? What would make the answer wrong? What should a reviewer be able to check?

Task: 07.03 Ask for critique before revision
Audience: who will use the output
Context: what the AI tool needs to know
Output: the exact format you need
Review: how you will check whether the answer is usable
Stop condition: when a human must decide instead of the tool

Common Mistake

The common mistake is treating the visible output as the final answer. A table may be incomplete. A chart may hide scale or sample-size problems. A trained model may fit noise. An AI-generated workflow may sound polished while missing a key constraint. Your job is to check the work before accepting it.

What To Do Now

Create a short note with three lines: