Designing LLM Applications Around Token Constraints

Large language models have strict limits on how much text they can process at once, and every token carries a financial and computational cost. To build scalable AI applications, you must learn how to work intelligently within these boundaries. This text-based course guides you from the foundational concepts of tokenization to practical strategies for managing context windows. You will learn how to optimize your prompts, structure your data, and implement smart retrieval patterns to build cost-effective and highly performant applications. What you'll learn: - Understand how tokenizers process text and calculate context window limits - Implement chunking strategies to split large documents without losing semantic meaning - Apply sliding window techniques and dynamic summarization to maintain conversation history - Design retrieval-augmented generation (RAG) patterns using vector databases to inject relevant context - Practice token-efficient prompt engineering to minimize costs and improve response quality - Manage API rate limits and optimize latency in production environments You will start with the core concepts of tokens and prompt limits before moving step-by-step through practical architecture patterns, written code-based examples, and text-processing workflows. This course is designed for software developers, product managers, and AI enthusiasts who are beginning their journey with language model APIs. No advanced machine learning background is required. Start reading today to build smarter, more efficient AI applications that scale.