Managing RAG Context Windows and Token Limits

Large language models have strict limits on how much text they can process at once, and sending too much irrelevant data leads to poor performance and high costs. This text-based course shows you how to design efficient Retrieval-Augmented Generation (RAG) systems that deliver highly accurate answers without exceeding token limits. You will transition from building basic, unoptimized AI systems to engineering smart, context-aware RAG pipelines. By mastering text compression, intelligent retrieval, and structured prompting, you will dramatically improve response quality while keeping computational costs low. What you'll learn: - Understand the fundamental mechanics of LLM context windows and tokenization. - Implement prompt compression techniques to eliminate redundant information. - Apply Thread of Thought prompting to guide models through complex context. - Configure vector databases to retrieve only the most relevant document chunks. - Design context-filtering strategies that prevent information overload in AI prompts. - Practice writing clean, optimized prompts through step-by-step written exercises. The course begins with foundational definitions of tokens, context windows, and RAG architectures before moving into practical strategies for data filtering, compression, and advanced reasoning prompts. This course is designed for beginners in AI development and natural language processing; no advanced programming experience or prior machine learning background is required. Start reading today to build smarter, more cost-effective AI applications.