Phase 0: The AI-Native Engineering Mindset · 40 min · Claude Code · Anthropic API · Python
The Sycophant Problem and the Struggle Principle
A model that always agrees with your framing isn't confirming you're right — it's removing the one signal that would have told you you're wrong.
What you will learn
- Define model sycophancy and explain why RLHF-trained models are prone to agreeing with a user's framing rather than correcting it
- Recognize the 'can demo but can't debug' skill gap that unlimited AI-does-the-thinking produces
- Explain why this course sequences struggle before automation, and what a predict-before-reveal rep is for
- Run a sycophancy stress test that measures whether a model's diagnosis changes based on a leading vs. neutral framing of the same bug
Introduction
The Sycophant Problem and the Struggle Principle
Objective
Send the same buggy code to a model under a leading (factually wrong) framing and a neutral framing, then score whether the leading framing produces a worse, more agreeable diagnosis than the neutral one.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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