By themselves, pretrained Large Language Models (LLMs) are interesting objects of study. However, they need to undergo a subsequent transfer learning phase to make them useful for downstream applications. While historically referred to as “fine-tuning,” the range of the tools available to LLMs users to better adapt base models to their applications is now significantly wider than the traditional fine-tuning. In order to provide the reader with an idea of the strengths and weaknesses of each method and allow them to pick one that would suit their needs best, an overview and classification of the most notable methods is provided, specifically the prompt optimization, pre-prompting and implicit prompting (system prompting), model coordination through actor agents, integration with auxiliary tools, parameter-efficient fine-tuning, further model pre-training, from-scratch retraining, and finally domain-specific distillation.