AI coding tools are impressive.

But many teams are discovering something uncomfortable: when AI feels inconsistent, the problem often isn’t the model — it’s the system around it.

When documentation is fragmented, tickets are vague, architecture decisions are tribal knowledge, and context lives across multiple tools, AI simply amplifies the confusion.

In this session, we’ll explore why AI output quality is directly tied to process clarity and information architecture.

You’ll see:
• Real examples of how vague tickets produce vague AI output
• How structured specs dramatically improve generated code
• Why repository-centric documentation changes AI behavior
• The impact of ADRs and versioned documentation on model reliability
• How to design your workflow so AI operates with full, intentional context

This talk is not about prompt tricks.

It’s about designing engineering systems that are worth amplifying.

If AI multiplies whatever process you give it, the question becomes:

What is your process teaching it?

Attendees will leave with:
• A framework for evaluating whether their process is AI-ready
• Practical patterns for structuring specs and tickets for AI
• A documentation strategy that improves AI code quality
• A systems-first mindset for AI adoption