Task Statement 3.3: Describe the training and fine-tuning process for foundation models.

Training and fine-tuning a foundation model is a multi-stage process that starts with large-scale pre-training on unstructured data to build general language and reasoning skills. Fine-tuning then adapts the model to specific tasks or domains using labeled data, with techniques like instruction tuning, domain adaptation, transfer learning, and parameter-efficient methods (e.g., adapters). Success depends heavily on data quality—requiring careful curation, governance, representativeness, and sometimes reinforcement learning from human feedback (RLHF). Whether through full retraining or lightweight adjustments, fine-tuning enhances a model’s accuracy, safety, and alignment with business goals, making it practical for real-world applications.