How LLMs accelerate technical writing

In recent years, technical writing has introduced a variety of classic tools and technologies to optimize the creation process of technical documentation. These range from terminology tools for using or avoiding specific terms to the programmatic checking of writing and style rules and authoring memory systems, all of which are designed to support consistent language use.

Opportunities and potential of Large Language Models (LLMs)

The use of Large Language Models (LLMs) will facilitate access to information and make the creation of technical documentation even faster and easier. LLMs are trained to generate natural language and process enormous amounts of globally available text - which is also a challenge in a business context. LLMs have the potential to fundamentally revolutionize the way we deal with texts.

One challenge when creating technical documentation is formulating standardized and comprehensible texts. In many cases, technical information needs to be translated into an accessible language in order to reach a wide audience. LLMs can also help by suggesting more understandable wording and explaining complex concepts in a clear and catchy way. This leads to an improved user experience and helps technical documentation to be better understood by readers. However, the challenge is to train the models on the company-specific vocabulary and desired wording. This requires a large amount of training data coupled with a high level of resource input.

Another feature of LLMs is their ability to automatically predict text based on contextual cues. This enables them to generate text based on context and support the creation of sections and chapters in technical documentation. By identifying what would normally appear in a particular context, LLMs can provide suggestions and examples to assist technical writers in structuring text.

To realize this feature, as-built documentation tagged with metadata is very helpful, as the context in the query can be established via this metadata. This means that the model can search in the specific context, i.e. in the documentation with a similar metadata combination, and generate text suggestions.

Another advantage of using LLMs is that they continuously learn and improve. The more texts and information they process, the better they become at providing accurate and relevant answers. This means that technical writers can benefit from the continuous development of the models and quickly stay up to date with the latest technology.

Another challenge that technical writers often face is the tedious task of researching information in order to create accurate and complete technical documentation. This is where LLMs can serve as a source of knowledge by accessing a variety of technical and general texts and providing relevant information. This drastically reduces research time and allows technical writers to focus on creating high-quality documentation.

Challenges and responsible use of LLMs

Of course, there are also some uncertainties when implementing Large Language Models in the technical writing process. One of these is the quality of the generated texts. Although the models are making great progress, there is still a risk that inaccurate or misleading texts will be generated, especially if there is little information available on the requested topic.

Therefore, it should be carefully checked which texts are generated by the models and to what extent they are suitable for the respective requirements. In addition, the responsibility for the texts should still lie with the technical editors who, ashuman-in-the-loop, ensure that the texts meet the desired standards. But even then, a significant increase in efficiency is possible by shifting activities from creation to checking.

Another problem is the availability of relevant data. The performance of LLMs depends heavily on the data on which they have been trained. It is therefore important that sufficient high-quality technical texts are available for training the models to ensure that they provide accurate and reliable information.

Last but not least, the protection of the company's own data also poses a particular challenge. For example, it is not desirable for many companies to make their technical documentation available (before publication) for training publicly available models and thus potentially open it up to responses from other users.

Despite these challenges, the use of LLMs in technical writing offers a number of advantages: Editing processes are accelerated, the quality of documentation is improved and the ability to explain complex technical concepts clearly and comprehensibly is made easier.

Technical writing can benefit from GenAI technologies by supporting the generation of texts, the structuring of sections and chapters in documentation, and improving the accessibility and usability of technical texts. It is important that technical writers evaluate the possible uses of LLMs and ensure that the models meet their own requirements.

Overall, the use of Large Language Models offers an interesting opportunity to optimize the editing process and create high-quality technical documentation more quickly and easily. The continuous development of these models will help to make technical writing even more efficient and successful in the future.