This thesis explores the use of large language models (LLMs) to automate workforce scheduling through natural language interaction. The primary objective is tofine-tune a general-purpose LLM to generate structured scheduling data in JSONformat from natural language prompts. Using a parameter-efficient fine-tuningmethod (LoRA), we trained Microsoft’s Phi-4 models on a domain-specific datasetof Swedish scheduling requests. The model’s performance was evaluated acrossvalidation, test, and generalization datasets using structured accuracy and fieldlevel metrics such as F1 score. The fine-tuned model achieved 84% structuredaccuracy on the validation set and 81.74% on a generalization test set featuringdiverse scheduling scenarios. In contrast to previous work that relied on few-shotprompting, our approach emphasizes reliable structure generation followed byconstraint checking through external Python functions. Comparative results showthat the fine-tuned Phi-4 model outperforms OpenAI’s GPT models in accuracy,though at the cost of generation time. These findings demonstrate the feasibilityand effectiveness of a fine-tuned, locally deployable LLM for reliable and interpretable schedule generation.