LoRA (Low-Rank Adaptation)

A parameter-efficient fine-tuning technique that trains a small set of adapter weights (typically 0.1–1% of the full model) rather than updating all model parameters. LoRA enables domain-specific customization of large language models at a fraction of the cost and time of full fine-tuning—hours instead of days, $50–$500 instead of $10,000+. Multiple LoRA adapters can be swapped on a single base model, letting one deployment serve different agent behaviors (support tone, sales style, legal precision) by loading the appropriate adapter.