The Pitfalls of AI-Generated Design Documents and Technical Writing
Software engineer Colin Breck critiques the growing trend of developers using generative AI to automatically write design documents, pull request descriptions, and technical summaries after building software. He argues that reading these AI-generated documents is tedious and counterproductive because they dilute high-density human thought with low-density fluff. Clear technical writing forces engineers to refine their thinking, discover edge cases, and validate architectural decisions before implementation. Delegating writing to LLMs shifts an undue burden onto reviewers who must wade through verbose text, ultimately degrading engineering rigor and team communication efficiency. The critique highlights a reversal of workflow where AI is used to retrospectively summarize code rather than using writing as a tool for upfront design and problem-solving. This results in documents that mask a lack of underlying critical thought with polished, high-word-count prose.
## BACKGROUND
Technical design documents and pull request (PR) descriptions are critical communication tools in software engineering used to explain architectural choices, risk assessments, and code changes to teammates. With the advent of Large Language Models (LLMs) integrated into developer tools, engineers increasingly rely on AI to draft these documents automatically to save time.