Foundation model: pretrained, general-purpose. Fine-tuning adapts weights with labeled data.

Foundation model: pretrained, general-purpose. Fine-tuning adapts weights with labeled data. Prompting steers without changing weights. Start with prompting — cheaper, faster, no data needed. Fine-tune when prompting hits a quality ceiling, you have 100s+ examples, and the task needs consistent format/style. a document extraction pipeline achieved 98% accuracy with prompt engineering alone — fine-tuning was unnecessary.