Summary  
The chapter illustrates an iterative feedback loop for refining AI prompts—ask, evaluate output, diagnose gaps, and revise—using conversation memory to produce progressively more specific, actionable responses.

General domain of usage  
Team meeting facilitation

Your first attempt is rarely your best. Skilled AI users iterate:

**Ask → Read → Spot What's Missing → Ask Again**

This chapter is that loop — done intentionally.


## Round 1 — Start Vague (On Purpose)

Send this exact prompt:

>How do I run a better team meeting?

You'll get a generic list: have an agenda, end on time, take notes. Not useful.

The model didn't know who you are, what's actually broken, or what kind of answer you need. Same AI — weak prompt, weak output.

## Round 2 — Refine

Send a follow-up **in the same conversation**:

>More context: I'm a marketing PM. My weekly team sync runs 60 minutes but the last 20 are always quiet — people have already mentally checked out. Give me 3 specific changes I could try next week, each as one line: change → why → how to measure.

The answer becomes concrete, specific, and testable. Now you have something you can actually use.

## Round 3 — Push Further

>Now rank those 3 by lowest-effort-highest-impact. Tell me which one to try first.

Notice: you didn't repeat the context. The model carries it from earlier turns — this is conversation memory working in your favour.

## What You're Building

This isn't about getting one good answer. You're training a loop:

**Prompt → Output → Diagnose → Improve**

That loop is the real skill.

## Task

Run all three rounds. Watch how the answer evolves even though the topic stays the same.

Then repeat the pattern on your own work:

- **Round 1** — one sentence, no context;
- **Round 2** — add context, format, and constraints;
- **Round 3** — rank options, narrow scope, force a choice.

When you've completed one full cycle on your own task, move on.

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