Grounding AI Responses with Retrieval Augmented Generation (RAG)

In the rapidly evolving world of AI, ensuring your models provide factual, trustworthy, and contextually relevant information is crucial for reliable applications. This course will equip you with the essential techniques to 'ground' AI responses, connecting them to reliable external data sources and effectively managing large documents, so you can build more dependable AI systems. What you'll learn: * Understand the core concepts of AI grounding and why it's essential for factual accuracy. * Learn the principles of Retrieval Augmented Generation (RAG) and its architectural components. * Apply strategies for effectively chunking and embedding long documents for retrieval. * Practice crafting effective prompts to leverage retrieved information within AI models. * Configure simple RAG pipelines to integrate external knowledge into AI responses. * Evaluate the groundedness and relevance of AI responses using basic techniques. The course begins with foundational concepts, then guides you through the practical steps of building and evaluating grounded AI systems, focusing on real-world application. This course is designed for absolute beginners interested in AI, large language models, and building reliable AI applications, with no prior experience required. Start building more reliable and factual AI applications today.