We've all been there: GitHub Copilot promises to be your coding companion, but instead feels more like that overeager intern who confidently writes brilliant code for the wrong problem. As AI-assisted development tools become ubiquitous, the industry narrative promises revolutionary productivity gains. Yet many practitioners find themselves playing an exhausting game of context whack-a-mole—constantly explaining, re-explaining, and fixing what their AI "partner" confidently got wrong.

Having spent considerable time wrestling with this gap between AI promises and reality, I've discovered that the real challenge isn't prompt engineering—it's context engineering. This talk explores why "vibe coding" is fundamentally broken and introduces the systematic discipline that separates magical AI experiences from expensive disappointments. Through the constraint-context matrix and real examples like the Breadcrumb Protocol, I'll demonstrate why human expertise in scaffolding, steering, and domain understanding isn't just relevant—it's the secret ingredient that makes AI actually work. You'll leave with practical strategies for engineering context systematically and a framework for building sustainable human-AI collaboration that leverages both your skills and the machine's capabilities.

- The Context Engineering Challenge: The fundamental problem of context misalignment between developers and AI assistants
- Why AI-Assisted Development Fails: Understanding the constraint-context matrix and the gap between AI promises vs. reality
- Context Engineering as the Solution: Moving from prompt engineering to systematic context management
- The Enduring Role of Operator Skill: Why human expertise, scaffolding, and steering remain essential
- The Breadcrumb Protocol: A concrete example of structured workflows that bridge the context gap