How Top Engineers Prompt AI: A Data-Driven Approach

🔥 Stop telling your coding agent to be "95% confident." It can't measure that—and it hurts results.

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.

🔥 Get Started: https://app.dreamflow.com

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.

If this helped, like, subscribe, and drop your favorite clarifying question patterns in the comments! 🙌