Mastering AI’s New Langauge
Number 19 in our skills for modern technical communicators series
In a world where artificial intelligence is turning content creation upside down, technical communicators stand at a crossroads. While some see Large Language Models (LLMs) as disruptive forces, others recognise them as powerful allies in crafting clearer, more effective documentation. At Firehead, we think the question isn’t whether to embrace these tools, but how to master them to enhance our fundamental mission: making complex information accessible and useful.
As we navigate LLMs, I came up with a poem to try to express the delicate balance we need to strike:
Words meet wisdom in digital space,
CJ Walker and Several AI Helpers
Where human craft and code embrace.
Through prompts and patterns, clear and bright,
We shape tomorrow’s words tonight.
What is a Large Language Model?
Consider a Large Language Model (LLM) as a sophisticated AI system that has read and analysed billions of texts, from technical manuals to scientific papers. Like a highly educated colleague who has absorbed vast amounts of knowledge, an LLM can engage with language in ways that seem human – following instructions and generating relevant content. This technology represents a significant leap forward from traditional automation tools, offering technical communicators new ways to approach their work.
These AI systems work through pattern recognition at a massive scale. They process language by breaking it down into smaller pieces and analysing how these pieces fit together in different contexts. During training, LLMs learn to predict what words or phrases should come next in any given sequence, developing an understanding of language structure, style, and meaning along the way. This predictive ability is what enables them to generate coherent text, answer questions, and assist with various writing tasks.
Think of it like learning a language through immersion – the more examples the system sees, the better it becomes at understanding and using language appropriately. However, unlike human learning, LLMs don’t truly “understand” meaning; they work by recognising and reproducing patterns they’ve seen in their training data.
The Rise of LLMs in Technical Communication
The technology landscape of technical communication is going through a seismic shift. New AI tools are entering our workspace daily, promising to revolutionise how we create and manage documentation. At the heart of this transformation lies a technology that’s captured the imagination of practitioners across industries.
Large Language Models (LLMs) have emerged as powerful tools that are reshaping how technical communicators create, edit, and manage documentation. From ChatGPT to specialised documentation assistants, these AI-powered systems are not just changing workflows—they’re creating new opportunities for the technical communicators who learn how leverage them effectively.
As AI capabilities expand, many professionals wonder about the future of technical communication. The reality is that LLMs aren’t replacing technical communicators; they’re augmenting their abilities and opening doors to new specialisations. Technical communicators who develop LLM skills position themselves at the intersection of traditional documentation expertise and emerging AI technology.
Let’s explore the essential LLM skills that can enhance your technical communication career, from basic capabilities to advanced applications. We’ll look at how these skills can transform your daily work, create new career opportunities, and help you deliver more value.
The Current State of LLMs in Technical Communication
The integration of LLMs into technical communication workflows has evolved rapidly since the public release of ChatGPT in late 2022. Today, technical communicators use these tools across various aspects of their work, from content creation to quality assurance.
Common LLM Tools in Technical Communication
The LLM landscape for technical communication ranges from general-purpose tools to specialised solutions:
- General-purpose LLMs: ChatGPT, Claude, and Bard serve as versatile assistants for ideation, research, and initial drafts
- Documentation-specific tools: Products like Document360 and Paligo now incorporate AI features for content enhancement
- Custom solutions: Organisations are developing proprietary LLM systems tailored to their documentation needs and style guides
Current Applications in the Field
Technical communicators are primarily using LLMs in four key areas:
- Content Development
- First-draft generation for routine documentation
- Converting technical specifications into user-friendly instructions
- Creating consistent terminology across document sets
- Content Enhancement
- Style and clarity improvements
- Accessibility checking
- Localisation preparation
- Research and Analysis
- Quick information gathering from technical sources
- Identifying documentation gaps
- Analysing user feedback patterns
- Quality Assurance
- Consistency checking across documentation
- Technical accuracy verification
- Compliance with style guides
This rapid adoption is reshaping expectations around productivity and creating new standards for technical communication workflows.
With LLMs becoming central to our work, the question becomes: what skills do technical communicators need to thrive in this AI-enhanced landscape? Let’s explore the essential capabilities that separate effective LLM users from those who merely scratch the surface.
Essential LLM Skills for Technical Communicators
Understanding and effectively using LLMs requires more than just knowing how to write prompts. Technical communicators face unique challenges that LLMs are particularly well-suited to address. From managing vast documentation sets to ensuring consistency across multiple product versions, these tools can transform how we work—but only if we develop the right skills to leverage them effectively.
The technical communication field demands accuracy, clarity, and adaptability. We need to maintain documentation across multiple platforms, adapt complex technical information for various audience levels, and ensure our content remains current with rapid product updates. LLMs can help with all of these challenges, serving as powerful allies in:
- Converting complex technical specifications into user-friendly content
- Maintaining consistency across large documentation sets
- Generating variations for different audience levels
- Streamlining localisation processes
- Identifying gaps in existing documentation
However, realising these benefits requires a skill set that goes beyond basic LLM usage. Technical communicators need to develop expertise that combines their traditional strengths with new AI capabilities. This expertise includes understanding LLM capabilities, maintaining quality control, and selecting the right tools for specific documentation needs.
Understanding the Power and Limits of LLMs
While LLMs can seem almost magical in their capabilities, successful technical communicators must understand both their strengths and limitations. These tools excel at pattern recognition, language processing, and generating human-like text, making them valuable for tasks like first drafts and consistency checking. However, they can also generate plausible-sounding but incorrect information (hallucinations), struggle with current events, and have limited context windows.
For technical documentation, this means knowing when to leverage LLMs for tasks like standardising terminology or generating alternative explanations, and when to rely on traditional methods and human expertise. Understanding these boundaries helps create efficient workflows that maximise benefits while minimising risks.
For example, when documenting a new API endpoint, a technical writer might use an LLM to:
- Generate the initial structure and boilerplate content
- Suggest multiple ways to explain complex parameters
- Create code examples in different programming languages
However, they would need human verification for:
- Actual endpoint specifications and responses
- Security considerations
- Rate limiting details
- Current best practices
Mastering Quality Control in an AI-Enhanced Workflow
With LLM-generated content, quality control takes on new dimensions. Technical communicators must develop robust verification strategies that go beyond traditional proofreading. This includes identifying common LLM error patterns, implementing systematic fact-checking processes, and ensuring all generated content maintains technical accuracy and brand voice.
Consider a technical documentation team managing a large software product’s release notes. Their AI-enhanced quality control workflow might look like this:
- Use LLMs to draft release notes from development tickets
- Run automated checks for terminology consistency
- Compare generated content against product specifications
- Have subject matter experts verify technical details
- Use LLMs to identify potential inconsistencies across the full documentation set
- Conduct final human review focusing on accuracy and completeness
The key is developing a reliable review workflow that combines automated checks with human expertise. This might involve using specialised verification tools, maintaining comprehensive style guides, and creating clear processes for validating technical accuracy with subject matter experts.
Selecting and Integrating LLM Tools
The LLM landscape is changing quickly, with new tools and features appearing regularly. Modern technical communicators need to develop skills in evaluating these tools against their specific needs and constraints. This includes assessing factors like:
- Integration capabilities with existing documentation systems
- Cost and usage limitations
- Security and privacy considerations
- Output quality and consistency
For example, a documentation team at a healthcare technology company recently evaluated different LLM solutions:
- They tested ChatGPT for initial drafts but found privacy concerns with sending data to external servers
- They explored Claude for its larger context window, useful for processing entire documentation sets
- They ultimately implemented a combination of tools:
- An on-premises LLM for sensitive content
- Public LLMs for general writing assistance
- Specialised documentation-specific AI tools for terminology management
More importantly, technical communicators must learn to integrate these tools into their existing workflows in ways that enhance rather than disrupt their productivity. This requires understanding how to combine different tools effectively and knowing when to use which tool for specific tasks.
While mastering these essential skills is crucial, it’s equally important to understand how to use them responsibly and ethically. Let’s explore the best practices and ethical considerations that should guide our use of LLMs in technical communication.
Best Practices and Ethics in LLM-Enhanced Technical Communication
As LLMs are integrated into technical communication workflows, establishing clear best practices and ethical guidelines is important. These tools bring new responsibilities and considerations to our work, particularly in areas of content authenticity, privacy, and quality control.
Responsible AI Usage
Technical communicators must approach using LLMs with transparency and accountability. This means being clear about how and when AI tools are used in documentation processes, ensuring proper attribution, and maintaining content integrity.
A typical responsible AI workflow might include:
- Documenting which parts of content creation involve LLMs
- Establishing clear guidelines for appropriate AI usage
- Implementing verification processes for AI-generated content
- Maintaining records of source material and AI contributions
For example, a technical documentation team at a software company could:
- Use version control systems to track AI-assisted content
- Maintain transparency logs about AI tool usage
- Create guidelines for acceptable vs. unacceptable AI applications
- Develop processes for detecting and correcting AI-generated errors
Privacy and Security Considerations
Working with LLMs requires careful attention to data protection and security. Technical communicators often handle sensitive information that shouldn’t be shared with external AI systems.
Best practices for privacy protection include:
- Sanitising prompts before sending to external LLMs
- Using internal or air-gapped systems for sensitive documentation
- Creating clear protocols for handling confidential information
- Regular audits of AI tool usage and data exposure
When documenting internal APIs:
- Use placeholder data instead of real examples
- Remove sensitive parameters from prompts
- Maintain separate workflows for public and private documentation
- Implement review processes specifically for privacy concerns
Maintaining Human Expertise and Oversight
Maintaining human expertise remains fundamental when dealing with such powerful technology. Technical communicators must balance automation with professional judgment and continue developing their core skills.
Effective oversight practices include:
- Regular skill development in both traditional and AI-enhanced methods
- Clear processes for human review and validation
- Ongoing evaluation of AI tool effectiveness
- Maintaining documentation quality standards
The goal is not to replace human expertise but to enhance it, creating documentation that benefits from both AI capabilities and human insight.
Understanding these best practices and ethical considerations also opens up new career opportunities. Organisations are seeking technical communicators who can navigate the complexities of AI-enhanced documentation responsibly. New roles and specialisations to adapt to the new technology landscape are emerging.
Career Development and Future Opportunities
We’re witnessing a fundamental shift in career opportunities,as LLMs reshape our work environment. This isn’t just about learning new tools – it’s about reimagining the role of technical communicators in an AI-enhanced workplace. Technical communicators who develop LLM expertise are discovering expanded roles, increased responsibility, and new ways to deliver value to their organisations.
The transformation is happening faster than many anticipated. Technical communicators who just a year ago were experimenting with basic LLM prompts are now leading AI integration initiatives and developing sophisticated documentation workflows. The pace of this evolution creates both challenges and opportunities for career growth.
Emerging Roles and Specialisations
At Firehead, we’re seeing entirely new positions emerge that combine deep documentation expertise with AI capabilities, creating hybrid roles that didn’t exist just a few years ago.
For example, a large tech company recently created these positions:
- Documentation AI Specialist
Develops and optimises LLM-enhanced documentation workflows, creates prompt libraries, and ensures AI tools align with documentation standards - Content Quality Architect
Designs review systems that combine AI verification with human expertise, establishing quality metrics for AI-assisted content - Documentation Workflow Engineer
Integrates AI tools with existing documentation platforms, automates routine tasks, and develops custom solutions for unique documentation challenges
These roles typically require:
- Deep understanding of technical communication principles and best practices
- Hands-on experience with LLM implementation in documentation workflows
- Ability to bridge technical and non-technical teams
- Strong focus on quality control and process improvement
Professional Development Path
Building LLM expertise is a journey that combines traditional technical communication skills with emerging AI capabilities. This journey requires a structured approach, but it also demands flexibility and continuous learning as the technology evolves. The key is to build a foundation that allows you to grow with the technology while maintaining the core principles of effective technical communication.
A typical progression might look like this:
- Foundation Building
- Master fundamental LLM concepts and capabilities
- Develop prompt engineering skills through practical exercises
- Study AI ethics and responsibility in documentation
- Learn to evaluate LLM output quality
- Practical Application
- Begin with small, low-risk documentation projects
- Experiment with different LLM tools and approaches
- Build a personal library of effective prompts
- Document outcomes and lessons learned
- Advanced Development
- Specialise in specific documentation types or industries
- Learn to customise and fine-tune LLMs
- Develop integrated workflows combining multiple tools
- Create custom solutions for complex documentation needs
- Leadership Evolution
- Guide teams in AI tool adoption
- Develop organisational AI documentation strategies
- Lead cross-functional AI integration projects
- Shape industry best practices
Building your portfolio:
- Create case studies of successful AI-enhanced documentation projects
- Develop metrics showing efficiency improvements and quality gains
- Document innovative solutions to complex documentation challenges
- Showcase leadership in AI adoption and implementation
Success requires more than just technical skills – it demands a strategic mindset and the ability to see both the possibilities and limitations of AI-enhanced technical communication. As you progress in your career, you’ll find yourself not just using AI tools, but shaping how they’re implemented in documentation workflows.
With these career paths and opportunities in mind, let’s consider how to begin or advance your journey with LLMs in technical communication.
Looking Ahead: Your Path Forward with LLMs
The integration of LLMs into technical communication represents more than just a technology shift – it’s a transformation in how we approach documentation, quality assurance, and professional development. As we’ve explored throughout this article, success in this new landscape requires a balanced approach: embracing AI capabilities while maintaining the core principles of effective technical communication.
Key Takeaways
The journey to LLM proficiency in technical communication involves:
- Understanding both the capabilities and limitations of LLM tools
- Developing robust quality control processes
- Maintaining strong ethical standards and best practices
- Building a strategic career path that leverages these new skills
Getting Started
The best time to begin developing your LLM skills is now. Start small:
- Experiment with basic LLM tools in non-critical documentation tasks
- Join communities of technical communicators exploring AI
- Document your learning experiences and outcomes
- Share knowledge with colleagues and peers
Further Learning Resources
Want to deepen your understanding of LLMs in technical communication? Here are some valuable resources:
Websites we use for reference here at Firehead:
- I’d Rather Be Writing (https://idratherbewriting.com)
Tom Johnson’s blog frequently covers AI and LLMs in technical writing - Write the Docs (https://www.writethedocs.org)
Community discussions and resources about AI in documentation - Technical Writing World (https://technicalwritingworld.com)
Forums and articles about emerging tech writing tools
The Road Ahead
As LLM technology continues to evolve, technical communicators who combine AI proficiency with traditional documentation expertise will be well-positioned to lead innovation in our field. The future belongs to those who can bridge the gap between human insight and AI capabilities, creating documentation that better serves user needs while maintaining the highest standards of quality and accuracy.
Remember: The goal isn’t to replace human expertise but to enhance it. By developing your LLM skills thoughtfully and strategically, you can help shape the future of technical communication while delivering more value to your organisation and users.
Develop Your Technical Communication Skills with Firehead
While LLMs are transforming our field, success with these tools builds on strong technical communication fundamentals. At Firehead, we offer comprehensive training to help you develop the core skills that will enable you to effectively leverage LLM technology in your work.
Our courses cover essential areas such as:
- Documentation strategy and planning
- Content design and development
- Quality assurance processes
- Technical writing best practices
These foundational skills are crucial for making the most of LLM tools in your documentation workflow. As LLM technology evolves, we’re also developing new courses specifically focused on AI integration in technical communication.
Want to stay ahead of the curve?
- Browse our current course offerings at The Firehead Training Academy
- Sign up for our newsletter (scroll all the way down to the bottom of the page) to learn about new AI-focused courses as they launch
- Follow our blog for the latest insights on modern skills in technical communication
Contact us here to discuss your learning path and discover how our training can help you thrive in the AI-enhanced future of technical communication.
Firehead. Visionaries of Potential.
