# How Top Engineers Prompt AI: A Data-Driven Approach

## Overview

In this Dreamflow Tips episode, I show a research-backed workflow that improves code quality, boosts visual fidelity, and cuts token/credit spend by making the model ask clarifying questions before it writes any code.

### TL;DR

Clarify → Then Code. Confidence thresholds ≠ correctness.

## What you'll learn ✅

- Why self-reported "confidence" ≠ correctness (the calibration problem)
- A simple clarify-then-code prompt you can reuse
- Two demos: complex UI + real bookmarking feature
- How clarifying questions reduced token/credit usage 📉

## Chapters ⏱️

0:00 Intro  
0:23 Why "ask clarifying questions" works  
1:35 Evidence from recent studies  
3:01 Why the "95% confident" rule fails  
4:02 Before/after: one-shot UI vs clarify-then-code  
6:21 Cost & fidelity improvements (credits/tokens)  
7:51 Real-world demo: bookmarking/favorites feature  
10:02 Summary + copy-paste prompt

## Copy-paste prompt 💬

"Before writing code, analyze my request. If there's any ambiguity about the best way to accomplish the task, ask clarifying questions. Do not proceed until I answer."

## Why it matters 🎯

Senior engineers de-risk ambiguity first. Making your agent do the same yields tighter scope, fewer wrong turns, and better final code.