Beyond the Algorithm: Why Human Technical Writers Remain Essential
AI can assist with documentation tasks, but it can’t replicate the crucial analytical skills, user advocacy, and nuanced understanding that define effective technical communication.
AI is the next big thing. Every day, there are new offerings and improved features. Every business unit is experimenting with AI to improve efficiencies, and technical writing is no different. The thought is that AI can generate accurate, usable documentation with targeted inputs, a little editing, and you've got complete documentation. It sounds appealing, but it isn't realistic. Technical writing is more than just putting words on a page. Our expertise and the human element are essential when creating usable, complete documentation.
We're already seeing its potential in areas like:
AI to assist with API documentation: Generating initial drafts of function descriptions or parameter lists.
AI to check for consistency: Ensuring uniform terminology and style across large document sets.
AI to help structure information: Suggesting logical flows and organizational patterns for complex topics.
AI to aid in localization: Providing initial translations, though requiring human review for accuracy and cultural nuances.
I've used AI for those tasks, but those aren't writing; it's just like mixing paint, which is not painting a house. Technical writers do what AI can't yet do. We:
Analyze complex systems and processes: We work with new hardware, software, and services to understand their functionality and purpose.
Identify target audiences and their needs: We write for specific users, considering their goals and requirements.
Plan and structure information: We design user-centric documentation that is logical, accessible, and usable.
Collaborate with subject matter experts (SMEs): We interview engineers, product managers, support teams, and other stakeholders to gather accurate information.
Test and validate documentation: We ensure the accuracy and usability of our deliverables through testing.
Manage documentation projects: We often oversee the entire documentation lifecycle, from planning to publication and maintenance.
We create various content formats: We produce traditional manuals, tutorials, knowledge base articles, API references, release notes, and more.
Advocate for the user: We act as the user's voice, ensuring that documentation meets their needs and helps them succeed.
AI can help with these, but it can't do them. You can't just give an AI specifications, user interviews, and other inputs and get a product and its documentation. It's not an assembly line. It's a good assistant for specific tasks but lacks critical thinking, contextual understanding, and user empathy, which are core components of effective technical communication.
Will it ever? I think so. As systems become increasingly complex and gain more 'experience,' we'll see them encroach more and more on traditional technical writing and editing.
But it's also being used by job candidates. We ask them for samples, so it is natural that they're turning to AI to help them.
Catching AI in Writing Samples: Looking Beyond the Surface
For hiring managers, evaluating technical writing candidates requires more than just checking a written sample's spelling, grammar, and structure. How can you tell the difference between human work and an AI-generated text? Here are some things to look for:
The Subject Matter is a Mile Wide and an Inch Deep: AI-generated content often sounds technically accurate on the surface, but it usually lacks the deep understanding and nuanced insights from human experience and research. You get a lot of information, but not a lot of the 'why' of usage and results.
Generic Language and Tone: AI tends to produce grammatically correct text lacking a distinctive voice. Look for writing that demonstrates critical thinking and original analysis. This leads into the next point.
Inability to Explain Choices: A crucial part of the interview process should involve asking candidates to explain their reasoning, research process, and the decisions they made in their writing sample. What problems did they have? Did they work under constraints? Someone relying on AI will struggle to answer and explain these choices.
Absence of User-Centric Considerations: Effective technical writing is focused on understanding and serving the user. AI-generated content can describe features but might not explain how they solve user problems. Look for evidence of audience analysis and a user-focused approach.
Inconsistencies or Odd Phrasing in Context: While AI can generate coherent paragraphs, longer samples sometimes reveal what's been called hallucinations, including an "off" style and facts that are just wrong.
Requesting Revisions And Explanations: Ask candidates to revise their sample based on a specific user scenario or technical constraint. Observe their ability to adapt and explain their changes. Someone who prompts an AI might struggle with this nuanced feedback.
Establishing Ground Rules for AI in Technical Writing Groups
Groups confronting AI must be proactive and honest about the benefits and disadvantages of using AI in their workflows. Establishing clear guidelines is essential for writing groups and teams looking to leverage AI responsibly. Here are some suggestions:
Transparency is Key: Always disclose when AI has been used in the any content creation shared within the group to foster trust and allow for appropriate feedback.
Focus on Augmentation, Not Replacement: Frame AI as a tool to assist with specific tasks (e.g., initial drafting, grammar checks, consistency checks), not as a replacement for human skills and critical thinking.
Human Oversight is Mandatory: Any AI-generated content must be thoroughly reviewed, edited, and validated by a human technical writer to ensure accuracy, clarity, and user-centricity.
Prioritize Ethical Considerations: Discuss and adhere to ethical guidelines regarding the use of AI, particularly concerning intellectual property and plagiarism.
Share Best Practices and Learn Together: Create a space for sharing experiences, challenges, and practical strategies for using AI tools in technical writing. Encourage experimentation and critical evaluation of different AI applications.
Define Clear Use Cases: Identify specific tasks or areas where AI can provide genuine value to the team's workflow without compromising quality or the core responsibilities of technical writers.
Continuously Evaluate and Adapt: The field of AI is rapidly evolving. Regularly revisit and refine your guidelines as new tools and best practices emerge.
Emphasize the "Why": When using AI, always consider the purpose and the intended audience. Does the AI-assisted content effectively meet their needs?
AI can augment the technical writing process, but can't replace it. Hiring managers must look beyond surface-level writing skills to assess critical thinking and user empathy. Establishing clear, ethical guidelines within our writing communities will enable us to use AI responsibly, ensuring that human expertise remains at the heart of effective technical communication. The future of technical writing isn't about replacing humans with AI, but about empowering skilled professionals with intelligent tools.
