Data Frameworks for Generative AI: Structuring Data for LLMs

Generative AI models are only as powerful as the data that feeds them. Without structured, high-quality, and well-governed data, even the most advanced large language models fail to deliver accurate results. This course guides you through the essential data frameworks required to support modern generative AI applications. You will transition from understanding raw data concepts to designing clean pipelines that feed retrieval-augmented generation (RAG) systems and autonomous agents, ensuring accuracy and security at every step. What you'll learn: - Understand the core terminology of generative AI data pipelines, including vector embeddings and chunking strategies - Configure data ingestion workflows optimized for retrieval-augmented generation (RAG) - Apply data cleaning and structuring techniques to prepare unstructured text for LLM consumption - Evaluate vector databases and indexing methods to ensure fast and relevant information retrieval - Implement data governance, privacy, and security standards to protect sensitive information in AI workflows - Analyze modern data pipeline patterns and basic LLMOps concepts for maintaining data quality over time The course begins with foundational definitions and key concepts of data preparation for AI. You will then progress through written explanations and structured text-based exercises that demonstrate how to design, store, and govern data for real-world AI applications. This course is designed for aspiring data professionals, software developers, and technical product managers who are new to generative AI and want to build a solid foundation in modern data frameworks. No prior experience with AI or machine learning is required. Start reading today to build the robust data foundation that modern generative AI demands.