Summary  
This chapter illustrates the concept of iterative refinement of user queries by engaging in follow-up dialogue to clarify vague requests and elicit precise requirements.

General domain of usage  
Data analysis

Most people throw a vague question at AI and get a vague answer back. Then they blame the AI.

In this lab, Claude is configured as a senior analyst who refuses to answer vague questions. It pushes back until your question is sharp.

>## Task
>
>Send Claude a deliberately vague question:
>
>><b>Our numbers look a bit off this month. Can you take a look?
>
>Claude will push back. Answer its follow-up. Three or four turns is enough.
>
>When Claude restates your sharpened question back in one line, **don't ask for the answer**. The reframing is the point.
>
>Done when your vague question is replaced by a sharp one.

A hands-on course for non-technical managers and operators who need to turn raw business data into decisions, not summaries. You'll learn how to frame analytical questions sharply, read spreadsheets and internal docs through AI, combine internal numbers with market context, and build your own analyst agent that produces decision-ready briefs end-to-end.

Introduction to business analytics through the lens of AI — why raw data doesn't make decisions, the three layers of analytical questions, and where an AI assistant actually fits in the workflow.



Train the framing reflex. Turn vague business questions into sharp ones, pick the metric that actually matters, and lock down time windows, segments, and comparisons before touching any data.



Hands-on labs with the three sources every operator hits: sales spreadsheets, strategy docs in Notion, and market context from the web. Single-tool labs first, then a combined investigation.



Move from running labs to building your own. Write the persona, wire up the tools, and create an agent that delivers weekly KPI briefs and pre-meeting decision packs — finishing with a capstone Quarterly Business Review.

