Building RAG AI Applications with Custom Data in AI Foundry

Generative AI is incredibly powerful, but to make it truly useful for your specific business needs, it must be grounded in your own proprietary data. This comprehensive text-based course guides you through the fundamentals of Retrieval-Augmented Generation (RAG) to connect AI models safely to your unique data sources. You will transition from understanding basic generative AI to designing and implementing structured RAG pipelines. By learning how to ingest data, generate vector embeddings, and configure semantic search indexers, you will ensure your AI applications deliver accurate, context-aware responses without hallucination. What you'll learn: - Understand the fundamental architecture of Retrieval-Augmented Generation and document chunking strategies. - Ingest and structure custom datasets safely within AI Foundry environments. - Configure vector databases and search indexes to enable rapid semantic search capabilities. - Apply prompt engineering techniques to ground AI responses directly in retrieved context. - Evaluate the accuracy of your RAG pipeline to minimize hallucinations and improve response quality. The course begins with essential terminology and the conceptual mechanics of modern vector searches. You will then progress through written step-by-step guides and code snippets covering data ingestion, index creation, and the integration of large language models to form a complete, context-rich AI solution. This course is designed for beginner developers, data enthusiasts, and AI hobbyists looking to build custom AI tools. No prior experience with vector databases or machine learning is required. Start reading today to unlock the power of context-aware generative AI with your own data.