Techniques

Fine-Tuning

The process of taking a pre-trained AI model and training it further on a smaller, specialized dataset.

Detailed Explanation

Fine-Tuning involves taking an existing foundation model (which has already learned general language patterns) and training it further on a specific dataset. This adapts the model to excel at a particular task, adopt a specific tone of voice, or understand niche industry jargon. Fine-tuning is more computationally expensive than prompt engineering but cheaper than training a model from scratch.

Related Terms

Large Language Model (LLM)

A type of foundational AI model trained on vast amounts of text to understand and generate human language.

RAG (Retrieval-Augmented Generation)

A technique that connects an LLM to external knowledge bases to improve accuracy and reduce hallucinations.

Back to Glossary