TECH Signal 503
Author argues AI should not replace the thinking process behind technical writing
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A practitioner advises against using AI to generate initial drafts, arguing that the act of writing is essential for deep thinking, while recommending AI for data preparation and post-draft feedback.
This perspective challenges the common workflow of prompting AI to produce documents from bullet points, suggesting that such shortcuts bypass the cognitive process that ensures content reflects the author's true intent. For engineers, it reframes productivity in writing not as volume of text produced, but as the quality of ideas communicated to teams and stakeholders.
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Generating documents from AI skips the critical thinking step required to deeply understand the problem being solved.
AI is effective for pre-writing tasks like data structuring and for post-draft tasks like asking clarifying questions, but not for initial ideation.
The value of technical writing lies in the impact of the ideas presented, not in the speed or length of the content produced.
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The core argument distinguishes between the mechanical act of producing text and the cognitive act of thinking through a problem. The author posits that writing is a tool for thinking, and outsourcing the initial draft to AI removes the friction that forces a writer to clarify their own understanding. This suggests that the quality of the output is directly tied to the depth of the author's prior mental processing, which AI cannot replicate.
The proposed workflow separates the roles of AI and the human author. AI is recommended for preparatory tasks such as sifting through data, structuring information, and finding patterns, which helps the author understand the problem space. However, the actual drafting of the document is reserved for the human, ensuring that the content reflects the author's specific insights and priorities rather than generic AI-generated prose.
After the initial draft is complete, the author suggests using AI as a feedback mechanism rather than a co-author. Instead of asking AI to make fixes or improvements, the recommendation is to ask it questions, such as what questions a reader might have or what the most important takeaway is. This keeps the author in 'thinking mode' as they decide how to address the feedback, similar to receiving proofreading from a colleague.
The argument addresses the concern that this approach is slower than using AI for generation. It counters this by redefining productivity in writing, stating that it is not about producing the most content in the shortest time. Instead, the value is measured by the impact of the ideas on the team or company, implying that a shorter, well-thought-out document is superior to a longer, AI-generated one that lacks core insight.
This view is not dogmatic about avoiding AI entirely, but rather about preventing the replacement of human thinking with AI 'thinking'. The author acknowledges that AI tools are great for coding, research, and data collection, but draws a firm line at the stage where the author must synthesize their own understanding into a coherent narrative. This distinction is crucial for engineers who rely on documentation to communicate complex technical decisions.
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