Solving Knowledge Gaps in AI Models: RAG vs. CAG [video]

Summary

The video compares Retrieval-Augmented Generation (RAG) and Context-Augmented Generation (CAG) as methods to address knowledge gaps in AI models. It discusses how RAG retrieves external information to supplement model responses, while CAG enhances context within the model itself, highlighting the strengths and limitations of each approach. These advancements have significant implications for improving the accuracy and reliability of AI-generated content.