AI Scaffolding and the Systems Engineering Mindset

Large Language Models (LLMs) are impressive, but they're also limited in important ways. In this clip, Chad Jackson discusses the concept of “AI Scaffolding”—a technique to solve the context window problem by maintaining a living document of constraints and inputs across short AI sessions. We explore why this disciplined approach fits perfectly with the mindset of Systems Engineers, who thrive on traceability and models. Conversely, Chad explains why Mechanical Engineers might struggle with this shift, as their workflow often involves jumping straight from mental processing to 3D geometry without documenting the “in-between” steps. Key Takeaways: • The Context Problem: Why long AI threads lose memory and how “scaffolding” fixes it. • The Workflow Shift: How using AI effectively requires ending sessions frequently and documenting inputs externally. • Discipline vs. Intuition: Why Systems Engineers are culturally ready for AI workflows, while Mechanical Engineers may face a steeper learning curve.