I’ve been a solo technical writer managing SaaS documentation for 8 years, working with teams where efficiency and clarity matter most. As a “team of one,” my focus is on low-effort, high-impact methods for improving documentation quality with limited resources. My experience straddles human empathy and machine logic, and I’m using it to help people write docs that perform well in both worlds.
Say it with me: AI. But this talk isn’t about *using* AI; it’s about writing now that AI tools reshape how people find our content. For tech writers and docs teams with limited resources, this creates a challenge: making content work for both humans and machines without adopting expensive tools or overhauling workflows. I’ll share a case study where simple metrics and an optional Python script made documentation quality measurable. By focusing on consistent editorial changes, we achieved a 23% improvement in scores that affect usability, SEO, and AI discovery. No generative AI. No black boxes. Just small adjustments that made a big difference. You’ll leave with a practical framework for defining quality metrics, identifying high-impact edits, and measuring improvement. The approach works for public or internal docs, AI-forward or AI-skeptical teams. Writing for machines *is* writing for people, because clarity is universal.
