LLM Engineering: Prompting, Tuning, and Retrieval Fundamentals

Integrating large language models into real-world applications requires more than just basic API calls; it demands reliable, scalable engineering. Developers need to understand how to structure prompts, retrieve context dynamically, and adapt models for specific business domains. This text-only course guides you through the core architectural patterns of LLM engineering, transitioning you from writing simple prompts to designing robust, retrieval-driven systems that generate predictable, high-quality outputs. What you'll learn: - Understand key LLM concepts, tokenization, and foundational model architectures. - Apply advanced prompt engineering techniques to guide model behavior reliably. - Configure Retrieval-Augmented Generation (RAG) systems using vector databases. - Evaluate when to use fine-tuning versus prompting and retrieval strategies. - Design scalable architectures that manage latency, costs, and API limitations. - Implement modern evaluation frameworks to measure and monitor system performance. You will begin with foundational definitions and key terminology before exploring practical architectural patterns. The material progresses logically from basic prompting structures to complex retrieval pipelines and tuning workflows. This course is designed for software developers, data professionals, and aspiring AI engineers who are new to building production systems with large language models. No prior experience with machine learning is required. Start building stable, production-grade AI systems today.