AI coding assistants have changed the speed at which we write code, but they haven't changed what makes code good! In fact, they've raised the stakes. Repeated logic, unclear naming, and tangled dependencies don't just confuse new developers — they bloat the model context, confuse the LLM, and burn through your token budget while degrading the quality of every suggestion the AI makes. The result? Inconsistent, low-quality output that amplifies your existing technical debt.

In this session, we'll explore why the fundamentals of software architecture — DRY principles, separation of concerns, meaningful abstractions, and testable design — aren't just good hygiene, they are essential for developing quality, sustainable software with AI. We'll look at how to scaffold AI instructions (prompt markdown files, agents, skills) so your tool follows your patterns instead of inventing its own. And we'll do live, side-by-side comparisons: the same coding task with and without architectural scaffolding, so you can see the difference for yourself.

You'll leave with a portable set of LLM instruction templates you can drop into your .NET coding project today.