Designing LLM Applications with Retrieval, Context, and Prompts

Large language models are incredibly powerful, but unlocking their true potential requires more than just typing simple questions. To build reliable AI applications, you must master how models process context, retrieve data, and respond to structured prompts. This text-based course guides you through the foundational architecture of modern language model applications. You will transition from writing basic prompts to designing structured retrieval systems that feed accurate, real-time information directly into your models, ensuring precise and context-aware outputs. What you'll learn: Understand the core architecture of large language models and how they process context windows; Design effective prompt templates using modern engineering techniques and structured formatting; Implement Retrieval-Augmented Generation (RAG) to connect external knowledge bases to your AI applications; Configure vector databases and semantic search strategies to retrieve highly relevant information; Manage context limits and optimize token usage to build cost-effective and efficient AI systems; Apply evaluation methods to test the accuracy and reliability of your model outputs. Starting with essential AI terminology and foundational concepts, this written program takes you step-by-step through context management, prompt design patterns, and retrieval integration. You will read detailed explanations and analyze structured code examples to build a solid theoretical and practical foundation. This course is designed for beginners and aspiring AI developers, requiring no prior experience with machine learning or natural language processing. Begin reading today to start building smarter, data-driven language model applications.