LLM Apps for Data: Build a RAG Analytics Assistant

As datasets grow more complex, the ability to query and analyze information using natural language is becoming an essential skill. Adding an AI layer to your data workflows allows you to uncover insights faster and more intuitively. In this foundational written course, you will learn how to design and build a Retrieval-Augmented Generation (RAG) analytics assistant. You will move from understanding basic AI terminology to practically applying Large Language Models (LLMs) to your own datasets, transforming raw information into conversational intelligence. What you'll learn: - Understand fundamental LLM concepts, key terminology, and how they apply to data analysis. - Build a foundational Retrieval-Augmented Generation (RAG) architecture to query custom datasets. - Implement modern vector databases to efficiently store and retrieve data embeddings. - Apply prompt engineering basics to guide your AI assistant toward accurate and relevant answers. - Integrate modern dataframe tools to prepare and structure your data for AI consumption. - Practice writing code snippets that connect your data pipeline to an AI layer securely. The course begins with a clear breakdown of AI terminology and RAG fundamentals before guiding you through practical written exercises. You will read through step-by-step text explanations and code blocks to construct a reliable, AI-driven data pipeline. Designed for beginners and data enthusiasts, this course requires no prior machine learning experience. Start reading today to bring the power of AI to your analytical workflows.