When training a language model, the vast majority of use cases don't require training from scratch, as tens of thousands of pre-trained models are available online that can be used out of the box. However, some use cases benefit from fine-tuning or domain adaptation, which involves refining a pre-trained model on a smaller custom dataset. A language model is not a knowledge base, but rather a computational representation of natural language that enables computers to process human-like language. Pre-trained models can be deployed with frameworks like Haystack without modification or training, and their prediction quality can be evaluated using metrics such as the F1 score. Fine-tuning an existing model involves adding more data and tweaking parameters to increase its accuracy, while domain adaptation focuses on better understanding domain-specific languages. Data labeling is crucial for machine learning models, particularly in NLP, where annotated data can teach a model to handle challenging cases. The process of training a language model can be made easier with tools like Haystack's annotation tool and platforms like deepset Cloud.