You don’t need millions of examples to see good results, what matters most is the quality of your data. For many use cases, a few thousand well-labeled, consistent, and relevant examples are enough to fine-tune a large pre-trained model effectively.
Instead of trying to collect endless raw data, focus on cleaning it up and making sure it reflects your domain accurately. Start with a small set, test the outputs carefully, and expand only when you know exactly where improvements are needed.
This approach saves time, cost, and headaches while still delivering real business value.
