AI Context Engineering: Optimizing LLM Inputs and Prompts

As large language models become central to modern software, the ability to feed them the right information at the right time is a critical skill. Learn how to design, structure, and manage the inputs that drive accurate, context-aware AI outputs. This text-based course takes you from the absolute basics of language model behavior to practical techniques for managing context windows. You will understand how to structure prompts, leverage retrieval-augmented generation (RAG) patterns, and optimize token usage to ensure your AI applications are both efficient and highly accurate. What you'll learn: - Understand the core architecture of context windows and how large language models process input data - Design structured prompts and system instructions that guide AI behavior predictably - Apply retrieval-augmented generation (RAG) patterns to ground model responses in external knowledge - Manage token limits and optimize context density to reduce API costs and latency - Implement techniques for context caching and dynamic prompt assembly - Evaluate and debug context-related issues like hallucination and attention loss You will start by exploring foundational terminology and the mechanics of transformer-based models before progressing to practical strategies for structuring complex inputs, handling external data sources, and optimizing overall performance. This course is designed for beginners, software developers, and prompt designers looking to build a structured understanding of AI context management with no prior experience in machine learning required. Start reading today to build smarter, more reliable AI integrations.