Fine-Tuning

Fine-tuning takes a pretrained model and continues training it on a smaller, more focused dataset to improve its performance on specific tasks or domains. If pretraining is a general education, fine-tuning is professional training. You might fine-tune a model on a company's customer service transcripts so it understands the products, tone and how the team handles common issues. The model retains its broad capabilities but becomes noticeably better at the specific things that matter. Traditional fine-tuning updates all of the model's parameters, which requires significant computing resources - less than pretraining, but still substantial. The quality and composition of the fine-tuning dataset matters enormously: a few hundred high-quality, representative examples often outperform thousands of mediocre ones. There's also a risk of "catastrophic forgetting," where fine-tuning on new data causes the model to lose some of its original general capabilities. For businesses, fine-tuning is worth considering when you need consistent, domain-specific performance that prompt engineering alone can't achieve - but it requires investment in data preparation and computing resources, plus ongoing maintenance as needs evolve - it's not a one-time fix.