Practical AI Engineering: Build RAG and Prompt Pipelines

Many developers believe building custom AI products requires expensive and complex model fine-tuning, but most real-world solutions actually rely on smart orchestration. This text-based course shows you how to assemble powerful, reliable AI applications using existing large language models. You will transition from conceptualizing AI to actually building functional pipelines. By focusing on practical architecture, you will learn how to connect models to external data sources, orchestrate multi-step prompts, and validate outputs to ensure safety and accuracy. What you'll learn: Understand the fundamentals of Retrieval-Augmented Generation (RAG) and vector databases; Design robust prompt pipelines to guide language models through complex workflows; Implement response validation and guardrails to filter out hallucinations and errors; Connect external data sources to LLMs for context-aware generation; Evaluate AI system performance using structured testing methodologies; Compare the trade-offs between API-driven orchestration and fine-tuning. The course starts with foundational AI concepts and terminology before guiding you through vector search, prompt engineering, and response validation patterns. You will read through clear explanations, architectural patterns, and step-by-step code implementations. This course is designed for software developers, product builders, and tech-savvy beginners who want to build functional AI systems. No background in machine learning or data science is required, though basic programming familiarity is helpful. Start reading today and build your first production-ready AI pipeline.