Enterprises are under pressure to apply generative AI to their data, but most data platforms weren't designed to support retrieval-augmented generation (RAG)-based conversational systems. Built for dashboards and siloed reporting, they often lack context, structure, and governance that AI applications require. RAG-Ready Patterns for Data Platforms provides a practical blueprint for closing that gap, showing how to evolve fragmented data estates into AI-ready foundations delivering trusted insights. Drawing on real-world experience building one of Microsoft's largest data platforms, the authors present proven patterns for unifying data across silos, capturing metadata, and embedding governance. Rather than treating RAG as an application-level concern, this book reframes it as a platform capability. With clear explanations, architectural diagrams, and reusable playbooks, you'll learn to design platforms that enable developers to build scalable, retrieval-augmented AI systems grounded in enterprise data. Assess whether your data platform is ready for RAG-based AI applications Apply patterns like unified data models and metadata-first pipelines Use data quality signals and curated grounding to improve AI reliability Embed governance and access control into platform design Enable developers to build trustworthy copilots, chatbots, and agents