This talk will explore the interplay between **pre-training** large-scale Transformer models on general corpora and the subsequent **fine-tuning** on domain-specific tasks to achieve state-of-the-art performance. We will examine the trade-offs between these two stages, focusing on challenges such as overfitting, computational demands, and maintaining model generalization. Through empirical results from legal text analysis, we will demonstrate how fine-tuning can significantly enhance model performance, particularly when combined with strategies like data augmentation and retraining on incorrect predictions. Attendees will gain a deeper understanding of how to effectively leverage pre-training and fine-tuning for specialized tasks, optimizing performance while addressing practical challenges in real-world applications.