Claudia Sistig, a graduate of the Technical Writing program at Hochschule Hannover 
Dr. Jeremias Rößler, Professor for IT and Media Management at the University of Applied Sciences in Karlsruhe 
Sissi Closs, Professor of Information and Media Technology, Managing Director of C-Topic Consulting and inventor of Klassenkonzept-Technik®
AI in Practice
A summary of the IUNTC Talk on March, 24, 2024, by Claudia Sistig (l.) and Prof. Dr. Jeremias Rößler (m.), moderated by Prof. Sissi Closs (r.)
The most recent IUNTC event "AI in Practice" was an opportunity to delve into the current use of AI in TC practice. The event featured separate but complementary presentations by Claudia Sistig and Prof. Dr. Michael Rößler, and was moderated by Prof. Sissi Closs. The 49 attendees for the session joined in a lively question-and-answer session after the talks.
Claudia Sistig, a graduate of the Technical Writing program at Hochschule Hannover, has been working as a Technical Writer at Compart GmbH since 2011. In 2016, she advanced to managing an agile development team as a certified Scrum Master. Beginning in early 2022, she has been engaged in exploring the potential of AI-based content generation and management in the field of technical writing.
Dr. Jeremias Rößler, Professor for IT and Media Management at the University of Applied Sciences in Karlsruhe and former founder and CEO of ReTest GmbH, is an award-winning author of the book "Artificial Intelligence and Software Testing", a well-known keynote speaker, and an enthusiastic software developer.
Sissi Closs, Professor of Information and Media Technology, Managing Director of C-Topic Consulting and inventor of Klassenkonzept-Technik®, is a software documentation pioneer and one of the leading experts in technical communication.
She is a member of the standards group responsible for revising the 2651x series of standards. She is involved in many areas at tekom, for example on the advisory board for standards, on the editorial advisory board of 'technische
kommunikation', on the European Academic Colloquium Review Board, as a speaker, author and reviewer
Mai 2024 - written by Yvonne Cleary & Daniela Straub
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Harnessing AI in Documentation: Opportunities and Limitations
Artificial Intelligence (AI) presents a plethora of possibilities for documentation. From generating images to crafting code samples and text modules, AI streamlines various tasks, but demands nuance. While it is very effective for reviewing texts and suggesting links and topics, it is outstanding in areas like multilingual support and simplifying complex information for diverse audiences.
However, AI cannot be used without human intervention in several contexts, including creating legally binding documents and diagrams. Despite advancements, AI cannot currently provide accurate contextual information, often failing to provide crucial nuances needed for effective communication.
To optimize AI use, adjustments in documentation practices are imperative. Technical communicators have a role to play here in crafting content that can be more effectively parsed by AI, and therefore facilitating smoother integration into workflows, e.g. for powering chatbots and large language models. With effective input from technical communicators, e.g. to provide contextual cues, organizations can leverage AI's potential to enhance efficiency and accessibility, bridging gaps in communication across diverse linguistic and technical landscapes.
Optimizing Documentation for AI Integration: Key Insights and Best Practices
The implementation of AI in documentation and technical writing involves several key considerations. One crucial aspect is providing adequate context to enable the AI to identify the correct information. This involves adapting writing styles to be more AI-friendly, ensuring that the documentation includes specific details such as product names, operating system compatibility, and other relevant information to reduce errors and improve accuracy.
Moreover, for professionals such as technical writers, developing soft skills like problem-solving, adaptability, and continuous learning is essential. While technical communicators don’t need to become data scientists, having a foundational understanding of AI concepts, machine learning, natural language processing, and neural networks is beneficial. This understanding helps professionals grasp the capabilities and limitations of AI, allowing them to make informed decisions about integrating AI into workflows.
Given the rapid pace of AI development, staying updated with new models, features, and tools is crucial. Companies should establish AI processes and integrate AI into their workflows. This includes updating style guides to incorporate AI-friendly writing rules, ensuring that documentation is optimized for AI-based functionalities like chatbots.
In practice, AI setups may involve various tools and platforms, such as chatbots trained on documentation, image generation tools like Adobe Suite or Stable Diffusion, and AI assistants like AI Positron in Oxygen for documentation review. These setups may utilize indexes generated by Python scripts to provide context to answer user questions accurately.
Implementing AI effectively requires practical solutions that prioritize minimalism, and provide users with the information they need efficiently. By addressing these considerations and continuously adapting to advancements in AI technology, companies can leverage AI to enhance their documentation processes and improve user experiences. Technical communicators have an essential role in these developments.
The Future of AI in Software Development: A Glimpse into Automated Systems
As we navigate the evolving landscape of artificial intelligence (AI) in software development, a shift towards more automated systems becomes evident. Gone are the days of directly interfacing with AI models; instead, we're ushering in an era of specialized systems that harness the power of AI to deliver precise functionality.
Consider a scenario where a developer needs to implement a new feature on a large website. Instead of manually writing code, they interact with a sophisticated AI-powered system. By providing specific prompts, the system intelligently divides the task into smaller components and sends relevant requests to the AI model. This approach enables the AI to generate or adapt code across multiple files, seamlessly integrating the desired functionality into the existing codebase.
AI can handle a remarkable breadth of tasks within this framework. From generating HTML and CSS to crafting database queries, AI is versatile in tackling the full stack of software development. Developers are then free to focus on high-level design and innovation, rather than mundane coding tasks.
Looking ahead, the implications of this paradigm shift are profound. Software development is poised to become more accessible and democratized, allowing non-traditional developers to participate in creating custom solutions. Imagine a world where anyone can effortlessly generate software tailored to their needs, without the need for extensive coding expertise.
This democratization of software development may lead to a proliferation of customized solutions, replacing standardized platforms with bespoke applications. However, it does not suggest that software developers will be redundant. Just as the advent of LEDs dramatically increased the use of lighting, AI-driven software development could spark a surge in innovation and adoption.
In essence, the future of AI in software development promises not the replacement of developers, but rather an amplification of their capabilities. By harnessing the power of AI within specialized systems, a new era of creativity, efficiency, and customization in software development is possible.
AI-Driven Software Development: A Vision for the Future
In recent years, there has been a paradigm shift in software development, moving towards AI-driven solutions that streamline the creation process and enhance functionality. Rather than relying on a myriad of plugins for additional features, the future lies in instructing AI to tailor software to specific needs, cutting down on unnecessary complexity and bloated codebases.
At present, AI possesses the capabilities of a junior developer, but with ongoing advancements, it will likely evolve into a sophisticated software architect. This trajectory suggests a future where standard software becomes tailored to specific industries, such as banking or healthcare, ensuring compliance with rigorous safety standards while leaving room for customization in less critical applications.
Looking ahead, it is possible that users will simply communicate their needs to the AI, which will execute tasks seamlessly, from booking vacations to managing internal processes. To achieve this vision, AI must become more interoperable with existing systems, ensuring reliability and trustworthiness. Despite current challenges, companies are actively addressing these issues to pave the way for a more deterministic AI future.
In practical terms, AI-driven software development requires a shift towards more specialized processes tailored to specific tasks, enhancing communication between stakeholders and AI systems. While concerns about reliability and ownership persist, implementing safeguards such as multiple AI checks and transparent AI-generated labels can mitigate risks.
Transitioning towards AI-driven software development entails a learning curve, as teams adapt to new workflows and technologies. However, with dedication and a proactive approach to skills development, teams can use AI to change the way software is created and used.
In conclusion, AI-driven software development will transform the industry, offering efficiency, customization, and functionality. By embracing this paradigm, organizations can unlock new possibilities and drive innovation in the digital landscape.