Table Question Answering with LLMs: Querying Structured Data

Tabular data is everywhere, but querying it often requires complex SQL or programming knowledge. Table Question Answering (Table QA) bridges this gap, allowing you to extract insights using simple, natural language queries powered by Large Language Models (LLMs). In this text-based course, you will transition from understanding basic data structures to building intelligent systems that can read, interpret, and answer questions about tabular datasets. You will gain a clear mental model of how LLMs interact with structured data and how to design robust pipelines for real-world downstream tasks. What you'll learn: Understand the foundational concepts of Table QA and how LLMs process structured data; Format and prepare tabular data using modern data libraries; Apply prompt engineering techniques specifically optimized for reasoning over rows and columns; Implement Retrieval-Augmented Generation (RAG) patterns tailored for large tables; Evaluate the accuracy and reliability of LLM-generated answers against your source data; Practice building end-to-end Table QA pipelines through written code examples. The course starts with essential terminology and structural definitions before guiding you step-by-step through data preparation, model selection, and prompt design. You will progress through structured text-based lessons and practical code snippets designed to reinforce your learning. This course is designed for beginner developers, data enthusiasts, and analysts who want to leverage LLMs for data querying, with no prior experience with natural language processing required. Start reading today to unlock the power of natural language querying for your tabular data.