RAG continues to rise

This podcast episode explores the evolution of generative AI workflows, the distinction between traditional machine learning (ML) and generative AI workloads, the debate between fine-tuning and retrieval augmented generation (RAG) models in AI, the trend towards a multi-model future in enterprise AI, the importance of multi-model capabilities for organizations, the challenges of evaluating AI models and the diverse nature of AI communities. The episode also discusses the implementation of role-based access control (RBAC) in vector databases, the limitations of transformers, the rise of neuromorphic computing as an alternative approach in AI architectures, Intel's leadership in neuromorphic computing, and upcoming events such as the MLOps Community podcast and an AI quality conference featuring notable speakers.