As a Developer Advocate, I can do many things with the help of AI, which will eventually make my life easier and give me more time to focus on work that requires my utmost attention.
AI can help me write code, prepare for talks, generate new ideas, and, shockingly, improve my Documentation. But should we use it to create docs? What technical documentation can AI produce, and what kinds of documentation always need more extensive human intervention? So many questions but limited answers. Let's answer them all with this article.
The four types of technical documentation and the role of AI
Tutorials: They are beginner guides(assume no prior knowledge) that teach how to implement a specific action; you can complete the task by the end. AI can help create tutorials by automating the generation of detailed instructions based on existing content or data.
How-to guides: differ from tutorials because they require prior knowledge and focus on helping users complete a specific task or learn about a new feature. AI can process documents and analyze prior documentation to create task-oriented how-to guides.
Explanations: Explanations help users develop a deep, broad, and detailed understanding of a complex concept or feature. Explanation is an area where AI is limited. It can help analyze data from multiple pages, extract data, and summarize key concepts quickly.
References: Reference pages are the go-to source for specific, detailed information. AI can handle structured data, process pages, and extract particular details to help create and maintain concise, organized, and easy-to-navigate reference materials.
Where AI falls short
One of the most significant shortcomings of documentation AI is its inability to understand the context in which technical documentation is created. AI-powered tools may be excellent at processing structured data and generating standard documentation. Still, they lack nuance and the ability to grasp the deeper context of the product or project.
AI can also hallucinate. For example, it might solve a problem that doesn't exist or incorrectly solve it using past data.
The right way to create software documentation with AI
1. Provide as much context as possible
You want the AI tool to have all the relevant details. Explain the project, specific requirements, and nuances involved in the task.
2. Define the objective clearly
State precisely what you wish the AI tool to produce.
3. Use examples
Give examples of the style, tone, or structure you want the AI to follow to maintain consistency with other documentation.
4. Iterate and refine again
Continue to refine, clarify, and tweak your requests based on the output. This ensures the final content is polished and aligned with your intended goals and audience.
Mintlify’s MCP servers: Striking the perfect balance
The rise of Model Context Protocol (MCP) introduces a new way to balance Human and AI by combining the conversational, iterative experience of AI tools with the context of your documentation.
Mintlify auto-generates MCP servers for your docs, turning them into a rich context layer that AI tools like Windsurf, Claude, and Cursor can tap into. With this setup, you can set the style and tone of the documentation, apply the 80/20 rule: let AI handle repetitive work while you focus on strategic editing, ensure consistency across your docs without manually referencing past content, and much more. More details are mentioned here.
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