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Technical writing has changed dramatically, moving from static manuals to dynamic online systems. The next big shift is already here. Many teams are looking at ai tools for technical writing to handle the more monotonous parts of the job. On the surface, this seems like a clear win. Who doesn't want to offload repetitive tasks?
Using ai for technical writing feels like the next logical step for creating content faster. But it's not always that simple. It's important to understand the relationship between ai and technical writing. Are there hidden complexities or downsides to consider before your team goes all-in?
We think the pros outweigh the cons. Here’s why:
How Has Technical Writing Evolved to the Age of AI?
They say the ancient Egyptians kept notes of their construction techniques, and we could argue that this qualified as technical writing. But the occasional stone tablet here and there is nothing compared to the plethora of instruction manuals, guidebooks, and helpdesks we see today.
Technical writing clearly entered a different realm with the rise of mass production and consumerism in the 20th century, aided by printing presses and typewriters.
The next major leap in the creation of technical content was led by the arrival of word processors in the 1980s. Prior to these machines, editors made changes by physically cutting strips of text out of a paper page and gluing alternative strips in the same location. Word processors retained the language of cutting and pasting but now use keyboard commands to rearrange text blocks almost instantly.
The internet then enabled content teams to centralize articles, images and reports in a content management system (CMS), allowing multiple team members to access, copy and manipulate the information. Extensible Markup Language (XML) then gave writers a way to label content types, so they could be stored, classified and pulled out for reuse in any kind of document.
In the first decade of this century, the CMS migrated from a local, wired system to an internet-based system–the cloud–retaining the same concept of shareable assets but now with better security, extra reach, and much lighter costs.
Smartphones greatly amplified the scope for online services, driving huge growth in the volume of technical content required to support platforms, apps and web-tools. Also, competition for page views forced any company with an internet presence to tailor its content for specific user types and ensure that it satisfied their queries, in other words, meeting user experience (UX) expectations.
So, we now have hundreds of thousands of companies creating online services, all racing to get their product out before the competition. If they can speed up any part of the content creation process, they will, especially repetitive tasks like copying and pasting the same text blocks.
AI tools can do just that, and can take care of many other aspects of content writing productivity and publishing.
Foundational Principles of Technical Writing
Even with AI assistants ready to draft content, the core principles of effective technical writing remain as important as ever. These fundamentals are what separate useful, user-friendly documentation from confusing walls of text. AI can generate words, but a skilled writer applies these principles to shape those words into something truly helpful. Think of them as the foundation upon which all good technical content is built. Without a solid understanding of these concepts, even the most advanced tools can produce content that misses the mark. Mastering them ensures your documentation is not just accurate, but also accessible and effective for your audience.
The 7 Cs of Effective Communication
A helpful framework for remembering these foundational principles is the "7 Cs of Effective Communication." This classic set of guidelines serves as a constant gut-check for technical writers, ensuring every piece of content meets a high standard of quality. Whether you're writing a simple step-by-step guide or a complex API reference, running your draft through the filter of the 7 Cs can highlight areas for improvement. It helps you shift your focus from simply getting the information down to communicating it in a way that truly serves the user's needs, which is the ultimate goal of any technical document.
Clear and Concise
Clarity is about making your message easy to understand. You should avoid jargon where possible and ensure you don't cram too many distinct ideas into a single sentence. The goal is to eliminate ambiguity so the user can grasp the information on their first read. Closely related is conciseness, which means getting straight to the point. Users are often looking for a quick answer to a specific problem; they don't want to sift through unnecessary words to find it. Adopting a structured authoring approach, where you create content in small, modular topics, naturally encourages both clarity and conciseness by forcing you to focus on one main idea at a time.
Concrete and Correct
To be concrete, your writing needs to provide specific details, facts, and examples that give the reader a clear picture. Instead of saying a process is "fast," specify that it "completes in under five seconds." This leaves no room for interpretation. Equally critical is correctness. Your documentation must be free of errors, with all facts, figures, and technical terms verified for accuracy. A single mistake can erode user trust and lead to significant problems. This is where a robust content governance framework becomes indispensable, establishing review and approval workflows to ensure that only vetted, accurate information reaches your audience.
Coherent, Complete, and Courteous
Coherence means your points are logically connected and flow in a sensible order, guiding the user smoothly through the information. Completeness requires you to provide all the information the user needs to understand a topic or perform an action, anticipating their questions before they have to ask. Finally, courteous writing maintains a friendly, respectful, and helpful tone. It shows you understand the user's perspective and potential frustrations. By writing with empathy, you create a more positive user experience and build a stronger relationship between the user and your product.
What Are the Current AI Capabilities for Technical Writers?
We believe most writers are interested in fitting AI into their content creation routine (which is why you’re reading this article!). So, how many ways are there to do this?
We see two main areas where AI transforms the writer’s work process: Automation of repetitive tasks and enhanced content creation, both of which can be broken down into multiple aspects.
1. Automation
- Content Generation: Anyone who’s heard of ChatGPT will know that AI can now generate basic drafts of technical documentation, such as summaries, descriptions, or simple instructions. This frees up writers to focus on higher-level tasks like analysis, complex problem-solving, and user experience design.
- Editing and Proofreading: AI tools to correct grammatical errors and stylistic inconsistencies are now built into the most popular document writing platforms, both online and offline. They give writers a free hand to be as creative as they wish, safe in the knowledge that their ideas can later be reworked into text that is easier to read. This also allows writers to dedicate more time to refining the content for better user engagement.
- Formatting and Layout: AI can automate formatting tasks, such as creating tables and lists or applying predetermined styles. Consistent layout and adherence to style guides contribute to user experience because readers can navigate more effectively through each document, helping them find what they want.
Enhanced Content
- Information Retrieval and Summarization: Companies accumulate reams of product information from the various teams involved in development. That’s a lot to process, but AI can quickly analyze it, extracting key insights and summarizing complex technical details. This helps writers gather information more efficiently and logically, ensuring that they avoid passages that waste the reader’s time.
- Content Optimization: AI tools for technical writing can analyze content for readability, accessibility, and SEO value, as well as recommendations for improvement. This ensures that documentation is user-friendly and easily discoverable.
- Personalization: The same content may need to be modified for different user roles, accounting for their past interactions and preferences. AI can be trained to understand those needs, guiding writers in targeting documentation for multiple use cases.
Categorizing AI Tools by Function
AI tools for technical writing aren't one-size-fits-all; they serve distinct roles throughout the content creation process. Thinking about them in terms of their core function helps clarify where they can provide the most value for your team. From polishing the final text to synthesizing complex source material, each category of tool addresses a specific challenge that technical writers face daily. Understanding these categories allows you to build a tech stack that supports your writers at every stage, rather than adopting a single tool and hoping it solves every problem. Let's look at the primary ways these tools are functionally grouped.
Writing Optimization and Editing
This is probably the most familiar category of AI tools. AI assistants that correct grammatical errors and stylistic inconsistencies are now standard in many writing platforms. They act as a safety net, giving writers the freedom to focus on conveying complex ideas without getting bogged down by mechanical errors. These tools can be configured to enforce your company's style guide, ensuring consistency across all documentation. This level of automated governance is critical for maintaining a professional and unified brand voice, which builds trust with your audience by providing a predictable and reliable reading experience.
Research and Information Synthesis
Technical writers often start with a mountain of raw information—engineering specs, developer notes, and user feedback. AI can be a powerful ally in this phase. As one writer noted, "AI can quickly analyze it, extracting key insights and summarizing complex technical details." This capability transforms the research process, helping writers gather and structure information more efficiently. Instead of spending hours manually sifting through documents, writers can use AI to surface the most relevant points, ensuring they don't create content that wastes the reader's time. This allows for a more strategic approach to managing your content from the very beginning.
Visual Content Creation
Clear visuals can make or break technical documentation. While AI isn't replacing graphic designers, it can handle many routine formatting tasks. For instance, AI can automate the creation of tables and lists or apply predetermined styles to ensure a consistent layout. This adherence to style guides helps readers find what they need quickly. Beyond basic formatting, some AI tools can generate diagrams, flowcharts, or other visual aids from simple text descriptions. This is especially useful for illustrating complex processes or systems, making it easier to publish content that is both informative and easy to understand across different channels.
Code Enhancement and Originality Checks
For documentation that includes code, AI offers specialized support. Certain tools can help with specific coding tasks, like generating HTML or CSS solutions, which saves developers and writers valuable time. This ensures that code examples are not only accurate but also well-formatted and easy to follow. On the flip side of creation is verification. AI can also check if your content has been copied from other sources, helping you ensure your work is original. This is a crucial step in maintaining content integrity and avoiding potential legal issues, reinforcing your overall content strategy and quality standards.
What is the Evolving Role of the Technical Writer with AI?
AI can augment content creators’ existing technical writing skills, but also creates instances in which writers will need to develop new competencies.
In the former case, AI can reinforce their ability as content strategists, for example, by analyzing user data and suggesting content structures that resonate with target audiences. Simultaneously, AI improves the writer’s role as an information architect by automatically tagging and organizing content for both human readers and machine processing. Furthermore, AI aids writers in becoming user experience experts by providing insights into readability, accessibility, and overall user engagement.
As for new skill sets, an important consideration is to recognize when AI is being helpful and when it isn’t. For example, AI tools are famed for repeating standard phrases and structures, making the content appear artificial, and they have been known to incorporate factually incorrect information in their output, referred to as "hallucinations." A key part of mastering AI-powered tools and platforms is understanding their limitations and how to effectively integrate them into the workflow.
Also, with AI generating data-driven insights about content performance and user behavior, writers have had to learn their own data analysis and interpretation skills. They need to know how to assess the reliability of the data the AI system is using and to verify the accuracy of its output.
This means another area of learning is "prompt engineering," which involves crafting precise instructions to guide AI in generating desired outputs. Moreover, a keen awareness of ethical considerations and bias detection is necessary to ensure that AI-generated content is inclusive and free from discriminatory language.
Practical AI Applications in Technical Documentation
It’s one thing to talk about AI in abstract terms, but it’s another to see how it fits into the day-to-day reality of a technical writer. The good news is that AI is no longer a far-off concept; it’s a practical tool that can be applied right now to solve common challenges and streamline workflows. From handling tedious, repetitive tasks to making global content delivery more efficient, AI applications are already making a tangible impact. The key is to move beyond the hype and identify the specific use cases where these tools can provide the most value for your team and your content operations.
For technical documentation teams, the most promising applications fall into three main categories: improving daily tasks and workflows, simplifying translation and localization, and enhancing the power of structured authoring. By integrating AI into these areas, writers can offload manual work and dedicate more of their expertise to strategic initiatives like content strategy and user experience. This shift doesn't just make the writing process faster; it makes the final product more consistent, accessible, and useful for the end user, which is the ultimate goal of any documentation effort.
Daily Tasks and Workflow Improvements
On a practical level, AI can handle many of the small but time-consuming tasks that fill a writer's day. Think about the effort that goes into ensuring every document adheres perfectly to your style guide. AI tools can automate formatting tasks like creating tables and lists or applying predefined styles, ensuring a consistent layout across all your documentation. This consistency isn't just about aesthetics; it helps readers find information more effectively. Similarly, advanced grammar and style checkers built into modern platforms can catch inconsistencies and errors, freeing writers to concentrate on the clarity and accuracy of the information itself rather than on mechanical edits.
Translation and Content Localization
Making content available to a global audience is a complex process, but AI is significantly simplifying translation and localization. AI-powered translation services can quickly convert documents into multiple languages, providing a solid first draft for human translators to refine. This speeds up the entire localization workflow and helps maintain a consistent voice and terminology across different regions. For teams managing multilingual documentation, AI can ensure that updates to a source document are flagged for translation, reducing the risk of outdated information reaching international users and making global content delivery more manageable and scalable.
AI in Structured Authoring and DITA
The combination of AI and structured authoring, particularly with a standard like DITA, is especially powerful. Because DITA-based content is already organized semantically as concepts, tasks, and references, AI can easily understand its structure and purpose. This allows AI to perform more sophisticated tasks than it could with unstructured text. It can analyze relationships between topics, identify opportunities for content reuse, and help enforce architectural rules. This synergy turns a content repository into an intelligent system where AI assists writers in creating, managing, and publishing highly organized and effective documentation.
Automating Content Classification and Reuse
One of the biggest benefits of DITA is content reuse, and AI can make this process even more efficient. An AI tool integrated into a Component Content Management System (CCMS) can analyze new or existing content and automatically suggest classifications, such as identifying a paragraph as a task or a concept. It can also find existing content fragments that could be reused in a new document, preventing writers from creating redundant information. By adding relevant metadata and linking related topics, AI helps build a more interconnected and easily navigable content library, which is fundamental to managing content at scale.
Assisting with Content Conversion
For teams looking to migrate from unstructured documents to a structured authoring environment like DITA, the initial conversion can be a major hurdle. AI can act as a valuable assistant in this process. It can analyze legacy documents, identify patterns, and suggest appropriate DITA structures for different sections of the content. While it won't handle the entire conversion perfectly on its own, AI can automate a significant portion of the initial work, breaking down large documents into smaller, topic-based components. This reduces the manual effort required from writers and makes the transition to structured content much less daunting.
Strategies for Using AI Effectively
Simply having access to AI tools isn't enough to guarantee better results. Like any powerful tool, its effectiveness depends entirely on the skill of the person using it. Adopting AI requires a strategic approach, not a passive one. Writers can't just push a button and expect perfect documentation to appear. Instead, they need to learn how to guide the AI, critically evaluate its output, and integrate it thoughtfully into their existing workflows. This means developing new skills and embracing a mindset of collaboration with the technology rather than simple delegation to it.
An effective AI strategy involves several key considerations. It starts with understanding how to communicate your needs to the AI through well-crafted instructions. It also means recognizing that a single, general-purpose tool may not be the best solution for every task. Building a toolkit of specialized AI applications often yields better results. Most importantly, it requires acknowledging that AI is a supplement to, not a replacement for, human expertise. The most successful teams will be those who learn to blend the speed and scale of AI with the critical thinking and deep subject matter knowledge of their technical writers.
The Importance of High-Quality Prompts
The quality of an AI's output is a direct reflection of the quality of the instructions it receives. In the world of AI, these instructions are called prompts, and learning to write them well is a critical skill. A vague prompt will almost always lead to a generic or unhelpful response. To get useful results, writers need to be specific. This includes defining the target audience, specifying the desired tone of voice, outlining the required format (like a step-by-step procedure or a conceptual overview), and providing relevant context. AI tools are only as good as the information they are given, so investing time in crafting clear, detailed prompts is essential for success.
Using a Mix of Specialized Tools
While general-purpose AI models are impressive, relying on a single tool for all your technical writing needs can be limiting. A more effective approach is to use a mix of specialized AI tools designed for specific tasks. For example, you might use one tool for grammar and style checking, another for summarizing technical documents, and a third integrated directly into your CCMS for DITA-specific functions like metadata tagging. This "toolbox" approach allows you to use the best tool for each job. Even with these tools, it's crucial to remember that AI doesn't replace the need for your own research and deep understanding of your customers' needs.
Supplementing AI with Human Expertise
AI is not going to replace technical writers. Instead, it will change the nature of the job, shifting the focus from pure content creation to a more strategic role. Writers will increasingly act as guides and editors for AI, directing its output and refining the results. The most important work will happen after the AI generates a first draft. This is where human expertise becomes irreplaceable: writers must review the content for technical accuracy, check for factual errors or "hallucinations," and add the expert knowledge and nuance that only a person can provide. This human oversight is a critical part of content governance and ensures the final product is trustworthy and truly helpful to the user.
What Are the Key Ethical Concerns of Using AI in Technical Documentation?
We can go into much greater detail about the ethical concerns of using AI for technical content, since it’s a disruptive technology that promises change in several areas:
1. Bias and Discrimination
AI models are trained on vast datasets pulled from the internet, which may reflect the biases of those who are more apt to comment online. This can lead to AI-generated content that is colored by political preferences, perpetuates social stereotypes, or excludes certain groups. Your team will need someone dedicated to curating the training data, but they too can use AI bias detection tools to do so.
2. Job Displacement
Since the purpose of AI is to automate the more tedious and repetitive writing tasks, there is a fear that it could lead to job displacement for some technical writers. However, as mentioned above, the same tools give writers the opportunity to upskill and reskill in new areas, such as content strategy or information architecture.
3. Privacy and Data Security
AI tools don’t necessarily know whether the facts and figures they compile could reveal sensitive details of unsuspecting individuals or organizations. Not everyone has PR teams vetting the information they publish. This issue can be mitigated by using secure AI platforms and anonymizing any data that could be construed as potentially sensitive. Again, there are AI tools on the market that are designed to ensure compliance with data privacy regulations.
4. Intellectual Property Rights
Companies copyright information because they know it will appear in the public domain, which is just where AI tools will read it. These tools can be trained to identify copyright symbols, watermarks, and other protective measures, but they struggle with subjective and nuanced concepts like "substantial similarity" and "originality," so it’s easy for copyrighted material to slip through the net. Companies should equip their writers with tools that can follow guidelines on intellectual property, but they have to be prepared to seek legal advice when necessary.
5. Transparency and Disclosure
The buzzword of our times is ‘misinformation,’ and internet users don’t take kindly to being strung along by evidently fake content that is presented as genuine. However, AI-generated memes are often shared partly because they are artificial. AI content is a valid option as long as it is labeled as such, and is trusted even more if your disclaimer mentions the actual tools and processes used.
6. Limitations and Managing Expectations
While AI offers exciting possibilities, it's not a magic wand. Understanding its limitations is key to integrating it successfully and responsibly into your workflow. Setting realistic expectations, both for yourself and for your management team, helps prevent misuse and ensures the technology serves as a helpful assistant rather than a source of risk. It's easy to get caught up in the hype, but a grounded approach will always yield better results. The most effective technical writers are those who see AI as another tool in their toolkit—one that requires skill and judgment to use correctly, just like any other. This means knowing when to use it, and more importantly, when not to.
Handling Confidential Information
One of the most important rules when using public AI tools is to never input confidential or proprietary information. Think of these tools as public forums; anything you share can be used to train the model and might resurface in unexpected ways. This means no unreleased product details, internal strategy documents, or personal customer data should ever be pasted into a prompt. Protecting your company's intellectual property is paramount, and feeding it into a third-party AI is a significant security risk. This practice is a core part of a strong content governance framework, ensuring that sensitive information remains secure while your team explores new technologies. Always operate under the assumption that what you share with an AI is no longer private.
Constraints in Regulated Industries
AI's capabilities face hard limits in highly regulated industries like biotech, energy, or finance. In these fields, documentation must adhere to strict compliance standards where absolute accuracy is critical. The risk of AI "hallucinations"—generating plausible but incorrect information—is too high when safety and legal standards are on the line. Furthermore, these industries require deep, specialized knowledge that isn't available in the public datasets used to train most AI models. This reality can sometimes clash with management expectations, where some leaders might view AI as a silver bullet to reduce headcount or speed up complex work. It's crucial for technical writers to set realistic expectations and explain that AI is a tool to assist, not replace, the essential expertise and rigorous review processes that ensure compliance and accuracy.
How Does AI Improve Knowledge Management for Content Teams?
Technical writing is focused on the presentation and usefulness of content, fashioning an end product from the raw material that is information. But not just any information - companies need to carefully curate the information that is relevant to their operation, and they do so with knowledge management tools.
Here too, AI can play an important role in organizing, storing, and sharing the pool of knowledge that technical writers draw on. The key functions in this respect include:
- Knowledge discovery: Extracting insights from vast amounts of data, identifying patterns and relationships, and prioritizing them in terms of value to the end user.
- Content categorization and tagging: Automatically organizing and classifying knowledge assets for easy retrieval or reference.
- Search: The easy retrieval mentioned in the last point is as helpful to the AI system as it is to human searchers. It’s one of the factors that helps AI tools compile and write content accurately and rapidly.
- Chatbots and virtual assistants: Instant access to the knowledge library means your AI system can answer user queries in real time.
- Knowledge graphs: Human search and discovery are greatly improved when we can visualize the relationships between concepts, ideas, and entities in our knowledge library, much like a mind map. AI excels at recognizing these connections and can rapidly guide users from one piece of relevant information to the next.
Where Do Human Technical Writers Fit in an AI-Driven World?
Even with the rapid advancement of AI in writing and content creation, human technical writers retain unique values that are difficult to replicate. Chief among our qualities is the capacity for creativity and originality, because humans are wired to make imaginative connections.
Much of our motivation in life is due to emotional responses, and in the case of writing we have an innate desire to connect with audiences on a personal level and build relationships.
Imagination and emotional connection go together to help us craft compelling narratives that will be remembered because they resonate with the reader.
Although we lean on computers and AI to analyze the elements of a problem, we probably still have the upper hand in critical thinking and solving complex problems that require nuanced understanding or contextual awareness.
And while AI machines have to be instructed to learn, we have a natural thirst for understanding. This makes humans highly adaptable to new technologies and tools, which often are the basis of new skills.
Technical writers can maximize this value by specializing in niche areas where human expertise is highly valued and by honing their creative skills. Everyone has their preferred method of stimulating creativity, but popular and proven options are:
- Taking breaks: Giving your mind space to think about something different
- Going for walks: Exposure to nature has a calming effect, releasing your mind from the dense problems of the office.
- Consuming culture: Movies, music, painting, and literature can all take you to another world. They can plant ideas in your unconscious mind that might help you view work problems from a different angle.
- Socializing: As above, but the real thing! Reality is stranger than fiction. Who knows what amazing tales and adventures lurk in your colleagues’ back-stories.
- Keeping a journal: Once you sit down to write, that’s when the inputs from the preceding activities pop up, possibly triggering ideas you can use at work
Is AI the Missing Piece in Your Routine?
Now that we’ve laid to rest any fears of AI forcing you out of a job, you’re probably wondering where AI could fit into your own production process. Enter Etto, Heretto’s revolutionary AI Copilot. Designed to work seamlessly within our platform, Etto brings intelligence and efficiency to every stage of content creation. From lightning-fast documentation creation and guideline adherence to content consistency and error detection, Etto ensures your workflow is smarter, faster, and more effective. Ready to see Etto in action? Schedule a demo today and experience the future of content creation!
Frequently Asked Questions
Is AI going to make my technical writing job obsolete? Not at all. Instead, think of AI as a collaborator that changes the nature of your work. It's excellent at handling the repetitive, time-consuming tasks like generating first drafts, checking for grammatical consistency, or formatting content. This frees you up to focus on the high-value work that requires human expertise: critical thinking, verifying technical accuracy, understanding user needs, and shaping the overall content strategy. Your role shifts from being purely a creator to also being a strategist, editor, and guide for the technology.
I'm ready to try AI. What's a good first step for a technical writer? Start with a small, specific task to see how AI fits into your current workflow. A great place to begin is by using an AI tool to summarize a long, dense source document from your engineering team. You could also use it to proofread a finished article against your style guide or to generate alternative headlines for a help topic. Choosing a low-stakes task like this helps you understand the tool's capabilities and limitations without disrupting your entire process.
You mentioned structured authoring like DITA. Is that necessary to use AI effectively? While you can certainly use AI with unstructured content, its true power is unlocked when paired with a structured authoring standard like DITA. Because DITA content is already broken down into modular, semantically-tagged topics (like concepts, tasks, and references), the AI has much more context to work with. This allows it to perform more sophisticated functions, such as accurately suggesting content reuse opportunities or helping to enforce your content's architecture. It's the difference between asking an assistant to organize a messy pile of notes versus a neatly labeled filing cabinet.
What's the most common mistake writers make when they start using AI? The biggest mistake is trusting the AI's output without question. You should always treat AI-generated text as a very rough first draft that requires your expert review. It can contain factual errors, subtle biases, or nonsensical information often called "hallucinations." Another critical error is inputting confidential or proprietary company information into public AI tools. Assume anything you enter could become part of the public domain, so never use it for sensitive material.
How does AI change my role beyond just writing faster? While increased speed is a definite benefit, the more significant change is the shift from tactical execution to strategic thinking. When AI handles the more mechanical aspects of content creation, you have more time and mental energy to dedicate to information architecture, user experience design, and data analysis. You can focus on whether the content is truly solving the user's problem, not just whether it's grammatically correct. Your role becomes more about guiding the entire documentation experience.
Key Takeaways
- Treat AI as your assistant, not your replacement: Let AI handle repetitive tasks like first drafts and formatting, so you can focus your expertise on strategy, technical accuracy, and creating a better user experience.
- Master the art of the prompt: The quality of AI output depends entirely on your input. Learn to write clear, specific instructions and use a variety of specialized tools to get the best results for each task.
- Pair AI with structured content for the best results: When AI works with organized, DITA-based content, it can perform more advanced work like automating content reuse and classification, making your entire content ecosystem smarter.
