"Leveraging AI in Technical Writing" is a practical guide for technical writers, documentation teams, proposal writers, and self-publishing authors who use artificial intelligence to produce manuals, procedures, specifications, proposals, and reports.
The book presents the Taming AI Methodology (TAIM), a repeatable seven-part workflow for controlling AI output rather than editing around it. AI models produce fluent, confident text whether or not the underlying facts are correct, so a writer who reviews only for tone can miss a real error. TAIM fixes the decisions that matter before drafting starts, so the constraint is built into the prompt instead of caught after the fact.
TAIM covers project setup, research, defining the goal, defining scope, outlining, drafting, and review. Each part pairs a decision the writer makes with a file that records it: a project instruction file, a research log, a goal statement, a scope list, an outline, unit prompts, and review findings. The files carry forward, so a decision made during scoping still holds when a section gets drafted. Together they become the writer's record of authorship, showing what was decided, when, and by whom.
Topics include prompt design for large language models, unit prompts, scope control, voice and reading level, model selection and its effect on grammar, AI hallucination, fabricated citations, and source verification. A chapter on workplace writing addresses accuracy, professional reputation, confidential data in unapproved AI tools, and review practice for documents that leave the building.
The publishing chapters cover AI disclosure questions, the conflicting AI content policies of Amazon KDP and Draft2Digital, automated screening of submitted manuscripts, United States Copyright Office guidance on the ownership of AI-generated work, and the use of AI to draft book descriptions, keywords, and categories.
Written in plain language with tables, figures, worked examples, and 47 cited references.
