Brit Van Blake has been a solo technical writer managing SaaS documentation for eight years, working with teams where efficiency and clarity matter most. As a “team of one,” her focus is on low-effort, high-impact methods for improving documentation quality with limited resources. Brit’s experience straddles human empathy and machine logic, and she’s 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 technical writers and doc teams with limited resources, this creates a challenge: making content work for humans and machines without adopting expensive tools or overhauling workflows.
In this session, 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. This approach works for public or internal docs, and AI-forward or AI-skeptical teams. Writing for machines is writing for people because clarity is universal.
