RAG and Fine-Tuning Explained

Successful AI integration in mission-critical systems requires data accuracy. The current best method for this is using Retrieval Augmented Generation (RAG) in combination with fine-tuned language models. In this short course you’ll get a high-level breakdown of how RAG and fine-tuning works, when they are useful, and where pitfalls are for these approaches.

Learning objectives
Define RAG and fine-tuning and explain how they work.
Identify use cases for RAG, fine-tuning, and a combination of the two.
Plan your AI strategy to incorporate RAG and fine-tuning in productive ways.