In this episode, Alexander Borek and Matthias Lein discuss the topic of multi-agent systems and how different AI applications can work together to solve complex problems. They talk about the rise of Gen.AI and the impact of models like ChatGPT. They also explore the two main methods used in AI applications: fine-tuning and retrieval augmented generation (RAG). Matthias explains how RAG works and its advantages over fine-tuning. They discuss the challenges of implementing and running RAG models, including data quality issues. They also touch on the limitations of RAG models and the future of AI. Multi-agent systems, powered by large language models (LLMs), are gaining traction in various industries. These systems involve multiple LLMs working together to perform different tasks and ensure checks and balances. The complexity of these systems requires careful ML operations and best practices. Industries such as marketing, sales, documentation, and manufacturing can benefit from multi-agent systems. The combination of LLMs in a multi-agent system allows for the automation of tasks that were previously difficult for non-coders. However, there are challenges in terms of cost, safety, responsibility, and reliability.