Fine-Tuning
Adapting a pre-trained AI model to a specific task or dataset by continuing training on new data, often improving task-specific performance.
A transfer-learning technique where a generic, large-scale pre-trained model (e.g., BERT, ResNet) is further trained on domain-specific labeled data with reduced learning rates. Fine-tuning accelerates development, requires less task-specific data, and leverages broad feature representations. Governance must track base-model provenance, license compliance, and document fine-tuning dataset and hyperparameter choices for reproducibility.
A legal-tech company fine-tunes a BERT model on 50,000 labeled legal-contract clauses. With only 1/10th the data of training from scratch, they achieve 90% accuracy on clause classification, enabling automated contract review that meets in-house QA standards.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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