Explore strategies for integrating domain-specific knowledge into large language models (LLMs) through pre-training, fine-tuning, and RAG.

Introduction to domain specific generative AI

Strategies for integrating domain-specific knowledge into LLMs

Domain specific pre-training for LLMs

Domain specific fine-tuning for LLMs

Examples

Costs: fine-tuning vs. pre-training LLMs

Enhancing LLMs with Retrieval Augmented Generation (RAG)

Example

Several strategies can be employed to integrate domain-specific knowledge into large language models (LLMs). These include pre-training the model, fine-tuning it, and utilizing Retrieval Augmented Generation (RAG).

What strategies can be used to add domain-specific knowledge into large language models (LLMs)?

Retrieval Augmented Generation (RAG) enhances generative AI models by providing contextual information from external sources, improving factual responses and reducing hallucinations.

How does Retrieval Augmented Generation (RAG) enhance generative AI models?

Domain specific generative AI: pre-training, fine-tuning & RAG

Domain specific generative AI: pre-training, fine-tuning, and RAG

Author

Steve Dodson

Articles

Domain specific generative AI: pre-training, fine-tuning, and RAG