We’ve all seen "AI Slop", like technical blog posts and documentation that look correct at a glance but are hollow, generic, or subtly wrong. As developers, we are being pushed to use LLMs to speed up our writing, but the real challenge isn't generation; it’s curation. How do you use AI to draft your READMEs, RFCs, and blog posts without sacrificing your technical authority?

In this session, we’ll move past the "Can AI write a talk?" gimmick and look at the actual workflow of a "Human-in-the-loop" creator.

We will explore:

- The Context Gap: Why AI fails at technical nuance and how to feed it the right "Architectural Context" to get meaningful drafts.

- The Editing Rubric: A 3-step framework for auditing AI-generated technical content to ensure it actually reflects your intent.

- The Ethics of Efficiency: When is it "cheating" to use AI for documentation, and when is it just being a productive engineer?

- Case Studies: Real examples of documentation and conference proposals—some human, some hybrid—and a breakdown of why the "hybrid" approach usually wins.

Stop using AI as a ghostwriter and start using it as a high-powered editor that still sounds like you.