Fine-tuning takes a pre-trained AI model and further trains it on your specific data to customise its behaviour, outputs, and domain expertise. Techniques like LoRA (Low-Rank Adaptation), QLoRA, and full fine-tuning specialise the model for your terminology, formatting requirements, and use cases, delivering competitive advantages without the cost of training from scratch.
Fine-tuning can improve model accuracy by 20-50% on domain-specific tasks compared to general-purpose models with prompt engineering alone. For tasks requiring consistent formatting, specific terminology, or adherence to brand voice across thousands of outputs, fine-tuning delivers the reliability that prompt engineering cannot match.
BespokeWorks provides fine-tuning services for organisations requiring customised AI models. Our fine-tuning process includes data curation, training configuration, evaluation benchmarking, and deployment, delivering models that understand your specific domain, follow your conventions, and maintain accuracy in production.