Despite ongoing digitization in industry, many companies still work with paper instructions or ‘paper-on-glass’ solutions (e.g., PDF files on screens). In recent years, various digital work instruction (DWI) technologies have become available that provide shop-floor employees with information during their activities, e.g., sequences of instructions for tasks at hand. Engineering new instructions in these systems for new products or product variants is however expensive and time-consuming. To scale up, there is a need for methods to generate work instructions (semi) automatically. Recently, Generative AI models and Large Language Models (LLMs) have taken center stage with their abilities to interact fluently with humans, both in understanding user questions/statements and in convincingly producing natural language texts. These models however suffer from several problems, including hallucinations where unsubstantiated content is presented as facts and lack of domain-specific data about products and procedures. For instruction generation however, we need verifiably correct statements about the task at hand. To tackle both problems, we have created a pipeline that combines the generative abilities of LLMs with explicit domain-specific data. We deploy a variant of Retrieval Augmented Generation (RAG) and incorporate an ontology that augments the instructions with additional information (policies, warnings, tools). Our results show an increase in correctness of output.