"- Think strategically, as in content strategy. The potential of AI content in certain scenarios, such as integration docs or code samples, is huge. Figure out where AI should interface in your information architecture and let the LLMs roam within the boundaries that you build for them. Shepherd AIs.
Test your assumptions, test everything. It’s already common knowledge that default LLMs’ output is good only up to a certain point, if not outright unusable. Even my kids can tell whether the stories GPT came up with are lame. Stage A/B tests and user research to verify how good LLMs really are.
Embrace metrics and docs observability. Don’t just unleash AI on a product and forget about it; instead, measure the impact of the AI-generated or AI-edited content across your product and content properties, see where they have the greater impact and where they could hurt your product’s credibility.
Hire with AI augmentation in mind. As I explained in Hiring technical writers in a ChatGPT world, writing skills are based on the same pattern matching and retrieval skills that LLMs mimic. Unless you expect writers to work offline on parchments, tolerate a certain degree of AI augmentation.
Advocate for your craft at work. Tech writers only write during a fraction of their time — the rest is spent chasing subject-matter experts, organizing information, and more. Don’t let stakeholders think that the deliverable is your job: Remind them how the cake is actually made."
