What’s the Difference Between Fine-tuning, Retraining, and RAG?

Customizing AI models with private data is a powerful way to enhance their performance for specific tasks. You can tailor the model to generate context-relevant content by training it with information such as industry-specific terminology or company-specific jargon.

This customization can lead to more accurate and personalized responses, predictions, or forecasts.

How they work in the real world

Techniques like fine-tuning, retraining, or Retrieval-Augmented Generation (RAG) open up various practical applications.

For instance, in customer service, a company can retrain …

ai ai models context customization data developer difference fine-tuning finetuning generate industry information jargon performance personalized predictions private data rag responses retrain retraining specific tasks tasks terminology training