How One Tech Writer Uses LLMs to Extract Knowledge, Not Generate Prose

A technical writer with over a decade of experience argues that the most valuable use of large language models in documentation is not drafting content, but accelerating the knowledge-extraction phase. The workflow involves feeding the model a messy corpus of existing docs, code comments, and meeting notes, then prompting it to identify gaps, contradictions, and undefined terms before any writing begins. Structured outlines mapped to documentation frameworks like Diátaxis are requested before narrative prose, keeping output task-oriented. A mandatory human review pass checks every fact against source material and rewrites sentences to suit the intended reader. The author contends that traditional documentation disciplines — versioning, user-task testing, and treating docs as a product — remain essential to keeping AI-assisted workflows rigorous and accurate.