Function Calling and Structured Data Extraction with LLMs

Large language models are incredibly powerful, but their raw text outputs can be difficult to integrate into software systems. By mastering function calling and structured data extraction, you can bridge the gap between unstructured natural language and reliable, machine-readable data. This text-based course guides you through the foundational concepts of tool use and structured outputs in modern AI applications. You will learn how to define precise schemas, handle API integrations, and parse LLM responses into clean data structures that your applications can use immediately. What you'll learn: - Understand the core mechanics of function calling and how LLMs interact with external APIs. - Design robust JSON schemas and use validation models to enforce structured data extraction. - Implement error handling and retry logic for parsing LLM outputs safely. - Connect language models to databases and external tools to build simple agentic workflows. - Apply best practices for system prompts to ensure consistent tool-calling behavior. Starting with foundational concepts of API schemas and structured outputs, you will progress through step-by-step written tutorials and practical coding exercises. You will read clear explanations, analyze real-world code snippets, and practice building integrations that allow LLMs to interact with external systems. This course is designed for software developers, data professionals, and tech enthusiasts who want to build production-ready AI applications. No advanced machine learning background is required; familiarity with basic Python and API concepts is all you need. Start reading today to unlock the full potential of structured LLM integration.