Reducing AI Hallucinations with Retrieval-Augmented Generation (RAG)

Large Language Models are incredibly powerful, but their tendency to generate convincing falsehoods—known as hallucinations—poses a major challenge for real-world applications. To build trustworthy AI systems, you must learn how to ground these models in factual, verified data. This text-only course guides you through the foundational principles of Retrieval-Augmented Generation (RAG), a powerful pattern that connects LLMs to external data sources. By understanding how to retrieve, process, and inject relevant context into your prompts, you will learn how to drastically reduce AI hallucinations and build reliable, facts-based AI applications. In this course, you will: 1. Understand the core concepts of AI hallucinations and why they occur. 2. Explore the architecture of Retrieval-Augmented Generation (RAG) and its key components. 3. Learn how vector databases and embeddings are used to index and search proprietary data. 4. Master basic document chunking strategies to optimize retrieval accuracy. 5. Apply prompt engineering techniques that instruct LLMs to rely strictly on retrieved context. 6. Evaluate model responses for truthfulness and factual alignment. You will start with essential definitions and core terminology before moving step-by-step through the mechanics of document processing, vector search, and prompt grounding. This course is designed for beginners, developers, and product creators who want to understand how to make AI applications more reliable; no prior experience with machine learning or vector databases is required. Start reading today to build smarter, safer, and more accurate AI solutions.