Foundations of RAG: Context Engineering and Reranking

Large Language Models are powerful, but they often struggle with accuracy and generate false information. Retrieval-Augmented Generation (RAG) solves this by providing relevant context, making AI applications reliable and factual. This text-based course guides you through the core concepts of building effective RAG pipelines. You will start with the fundamental terminology of vector databases and embedding models, then progress to practical techniques for context engineering and reranking. By the end of the course, you will know how to structure context effectively to prevent AI hallucinations and improve response quality. What you will learn: Understand the foundational concepts of Retrieval-Augmented Generation and vector search. Design effective context pipelines to provide accurate data to Large Language Models. Apply reranking techniques to prioritize the most relevant information for your prompts. Practice prompt engineering basics to guide models and reduce hallucinations. Implement modern retrieval patterns to improve the reliability of AI applications. The course flows logically from basic AI terminology and vector database concepts to hands-on written exercises in context structuring. You will read through clear explanations and code snippets that illustrate modern RAG architecture. This course is designed for beginners, aspiring developers, and tech enthusiasts with no prior machine learning experience required. Start reading today to build a strong foundation in modern AI development and context engineering.