Your AI copilot is brilliant in the moment but has the memory of a goldfish. Ask it to "add a new API endpoint" and it might choose REST when you're building GraphQL, scaffold Entity Framework when you're using Dapper, or forget your team's naming conventions entirely. Every. Single. Time. The problem isn't the AI—it's that you're starting from scratch with every prompt.

This session reveals the architecture behind production-grade AI workflows that maintain consistency across your entire codebase. You'll learn how to build "steering files"—external context documents that shape AI behavior without touching source code—and discover practical patterns for Product_Overview.md files, behavioral rule sets, and context management strategies. We'll explore real examples from healthcare software development: Cursor rules that enforce EARS requirements syntax, behavioral files that generate C4 architecture diagrams on demand, and guardrails that prevent AI drift during complex refactoring. You'll see how to structure context hierarchies so your AI knows when to write BDD tests, when to follow your team's coding standards, and when to generate comprehensive technical documentation.

Walk away with ready-to-use templates for steering files, a decision framework for what belongs in context vs. prompts, and battle-tested patterns for maintaining AI consistency across sprints, developers, and codebases. Whether you're using Cursor, GitHub Copilot, or Claude Code, you'll learn how to transform unpredictable AI assistants into reliable team members who remember your team's standards, architectural decisions, and quality expectations.