COMPEL Glossary / fine-tuning
Fine-Tuning
Fine-tuning is the process of further training a pre-trained AI model on a specific dataset to adapt it for a particular task or domain.
What this means in practice
For example, a general-purpose LLM might be fine-tuned on an organization's customer service transcripts to better handle industry-specific terminology and policies. Fine-tuning changes the model's weights, making it a more significant intervention than prompt engineering. It requires careful governance: the training data must be curated and validated, the model should be evaluated before and after fine-tuning to ensure no capability regression, and fine-tuned models should be versioned with clear records of training data and parameters. In the COMPEL framework, fine-tuning governance falls under model lifecycle management and requires staged deployment practices.
Why it matters
Fine-tuning enables organizations to adapt powerful foundation models to their specific domain, terminology, and use cases without building models from scratch. However, fine-tuning changes model weights and can introduce regressions, bias, or compliance issues if not governed properly. Organizations that fine-tune without evaluation discipline risk degrading the model's general capabilities while improving narrow performance.
How COMPEL uses it
Fine-tuning governance falls under model lifecycle management within COMPEL. During Model, fine-tuning decisions are documented with training data curation requirements. The Produce stage implements staged deployment practices with before-and-after evaluation. The Evaluate stage assesses fine-tuned model performance against both task-specific and general capability benchmarks, ensuring improvements in one area do not create regressions in others.
Related Terms
Other glossary terms mentioned in this entry's definition and context.