Explore the capabilities and limitations of Retrieval-Augmented Generation (RAG) in this 44-minute conference talk from EuroPython 2024. Delve into the reasons behind RAG's effectiveness in expanding Large Language Models' domain-specific knowledge while reducing hallucinations. Examine various use cases, from developer documentation chatbots to medical advice systems, and understand how their differing requirements impact RAG implementation. Learn to identify common failure modes, evaluate RAG's suitability for specific applications, and discover strategies to enhance output quality. Gain insights on when to consider alternative approaches, equipping yourself with a comprehensive understanding of RAG's potential and constraints in real-world scenarios.