Fine-Tuning

Fine-tuning is a transfer learning technique where a pre-trained model is additionally trained on a smaller, task-specific dataset to improve its performance on that particular task. For text-to-SQL applications, fine-tuning involves training the base LLM on pairs of natural language questions and their correct SQL translations for specific database schemas. This produces a model that better understands domain-specific terminology, schema conventions, and query patterns. Fine-tuning approaches range from full model fine-tuning (updating all parameters) to parameter-efficient methods like LoRA (Low-Rank Adaptation) that update only a small subset of parameters.