About Course
By their nature, large language models (LLMs) rely on the data they were once trained on. But if one wants to update that data to include up-to-date information or internal company information, they would need to retrain LLMs. This is a costly and laborious process. However, it was before Retrieval-Augmented Generation (RAG). RAG eliminates the need for retraining and is much cheaper to implement.
In this course, you’ll explore the main ideas behind RAG and its implementation process. You’ll learn the main concepts behind AI systems that optimize the output of LLMs by retrieving live, relevant information to enhance their output.
Through accessible explanations and real-world examples, you’ll learn how RAG bridges the gap between static AI models and dynamic knowledge sources. Whether it’s powering intelligent chatbots or enabling accurate customer support systems, this course provides the foundational knowledge to understand the transformative impact of RAG.
