A Reference Architecture for Deploying Large Language Model Applications in Industrial Environments

Integrating Large Language Models (LLMs) into industrial environments presents numerous challenges. Firstly, understanding the term LLM, its required components for implementation, and the various types of LLMs demands research effort. Furthermore, the processing of textual data involves enormous data volumes, and executing large language models requires substantial computational power. A significant challenge in model deployment on the shopfloor is the stringent data security measures, which often prohibit direct internet access to the cloud.To address these challenges, a reference architecture was developed to give an overview of the components for integrating LLM applications in industry. This architecture covers all essential steps of LLM applications, including data preparation, model training, model evaluation, model deployment, and prompt engineering. For model deployment, two possible locations were distinguished: the digital workplace, such as the office, and the shopfloor. In addition to model deployment, data preparation and model evaluation are emphasized as critical elements for the success of Large Language Model Operations (LLMOps) applications.This reference architecture enables project teams to better understand the elements involved in the LLM integration process. Furthermore, the universal approach helps in standardizing the associated processes, facilitating a more effective acceptance of LLMs in industrial environments.