Unstructured Data Processing for AI and RAG Pipelines

AI models are only as good as the data we feed them, yet most of the world's information lives in unstructured formats like PDFs, emails, and text files. Knowing how to extract, clean, and format this data is the secret to building reliable AI systems. This text-only course guides you through the foundational concepts of document processing and Retrieval-Augmented Generation (RAG), showing you how to prepare raw text for AI models to ensure accurate, context-aware responses. What you'll learn: - Understand the core principles of processing unstructured data for modern AI architectures. - Explore document AI techniques to extract clean text and structure from complex files. - Learn how chunking and embedding strategies impact the quality of retrieval-augmented generation. - Discover how vector databases store and retrieve semantic information for AI models. - Apply grounding techniques to reduce AI hallucinations and improve response accuracy. - Practice designing end-to-end data pipelines for AI applications through written conceptual exercises. We begin with key terminology and the foundational mechanics of unstructured data before moving step-by-step through extraction, vector storage, and RAG integration. You will study practical text-based examples and architecture patterns that you can apply to your own projects. This course is designed for beginners, software developers, and data enthusiasts who want to understand the data side of AI, with no prior machine learning experience required. Start reading today to master the data pipelines that power modern AI.