Large language models (LLMs) are very large deep learning models which are retrained on a huge amount of data. Among the LLMs are sentence bidirectional encoder representations from transformers (SBERT) where advanced training methods such as transformer-based denoising autoEncoder (TSDAE), generative query network (GenQ) and an adaption of generative pseudo labelling (GPL) can be applied. This thesis project aims to develop a semantic search tool for Swedish legal judgments in order to overcome the limitations of traditional keyword searches in legal document retrieval. For this aim, a model adept at understanding the semantic nuances of legal language has been developed by leveraging natural language processing (NLP) and fine- tuning LLMs like SBERT, using advanced training methods such as TSDAE, GenQ, and an adaption of GPL. To generate labeled data out of unlabelled data, a GPT3.5 model was used after it was fine-tuned. The generation of labeled data with the use of a generative model was crucial for this project to train the SBERT efficiently. The search tool has been evaluated. The evaluation demonstrates that the search tool can accurately retrieve relevant documents based on semantic queries and simnifically improve the efficiency and accuracy of legal research. GenQ has been shown to be the most efficient training method for this use case.