Building AI Apps with Node.js: YouTube and PDF Document Loaders

Modern AI models are incredibly powerful, but they are limited by the data they were trained on. To build truly useful applications, you need to feed them real-world, custom data from sources like YouTube transcripts and PDF documents. In this written course, you will learn how to ingest, process, and query external documents using Node.js and OpenAI. You will transition from working with static prompts to building dynamic, context-aware AI applications that can answer questions about specific videos and files. What you'll learn: Understand the fundamentals of document loaders and how they fetch external data; Extract and clean text transcripts from YouTube videos and PDF files using Node.js; Apply text chunking strategies to stay within LLM token limits and optimize search relevance; Generate vector embeddings to represent document meaning numerically; Query OpenAI models using retrieved context to generate accurate, data-backed answers; Implement modern asynchronous JavaScript patterns for efficient data pipeline management. The course begins with foundational concepts of data ingestion and token management before guiding you through step-by-step code implementation. You will read clear explanations, analyze practical script structures, and learn how to assemble a complete data-to-AI pipeline. This course is designed for beginner to intermediate Node.js developers eager to enter the AI space. Familiarity with basic JavaScript is helpful, but no prior experience with machine learning or AI APIs is required. Start reading today to unlock the potential of custom data in your Node.js applications.