Fine-Tuning

Also known as: Model Fine-Tuning, Domain Adaptation, Model Customization

The process of re-training a pre-trained AI model with domain-specific data to improve its performance on specialized tasks.

Fine-Tuning is the process of taking a pre-trained AI model trained on general data (a foundation model like GPT-4 or Llama 3) and re-training it with a domain-specific dataset to improve its performance on particular tasks.

In market research, fine-tuning allows creating specialized models for: classifying verbatims according to industry-specific taxonomies, generating reports in the tone and format of a specific company, sentiment analysis calibrated for a specific language or region, or coding according to proprietary research frameworks.

Fine-tuning requires high-quality training data (correct input-output pairs), computational capacity, and technical knowledge. More accessible alternatives are in-context learning through few-shot prompting and RAG systems, which achieve personalization without retraining the model.

Atlantia selectively evaluates fine-tuning for high-frequency tasks in its platform where precision is critical.