Data science series: An introduction to retrieval-augmented generation (RAG)

An introduction to retrieval-augmented generation (RAG): enhancing large language models with external knowledge

RAG is a technique that improves the reliability of AI systems by using extra information from external sources. Join our upcoming data science webinar to learn more about how RAG can improve the performance of large language models (LLMs) e.g. chatbots or generative AI systems, by generating responses based on a specific collection of documents or text.  

This session will cover: 

• What is RAG? Why is it needed? 

• How RAG addresses the limitations of traditional LLMs 

• Components of RAG - how the elements of RAG work together to produce more accurate evidence-based responses 

• Evaluation - how can we measure the performance of RAG? How do we know it’s working well? 

• A practical case study: applying RAG principles to easily chat with our data and receive evidence-based replies.