AI applications are only as reliable as the data that feeds them. But most guidance focuses on models, not on what happens before the model is called. In this course, you will learn how to ingest, validate, prepare, and format data for AI applications with confidence.

You’ll learn how to load text, CSV, and JSON files safely, and how to fetch data from web APIs while keeping credentials secure. You’ll explore how explicit data contracts with Pydantic make invalid inputs fail early, and how to clean tabular data with pandas, including missing values, duplicates, and inconsistent text. The course also covers how tokens and context windows limit what a model can process, how chunk size and overlap affect meaning, and how to format records and role-based prompts while reserving space for the output. What makes this course unique is its focus on judgment over syntax. Rather than just showing code, it explains why each decision matters, from encodings to chunk boundaries. By the end, you will be able to prepare reliable, well-structured inputs for any AI application.