Introduction to Retrieval-Augmented Generation (RAG)

Large language models are incredibly powerful, but they often lack access to your specific, real-time business data. Retrieval-Augmented Generation (RAG) bridges this gap, allowing you to ground AI responses in factual, private information without expensive retraining. In this course, you will transition from understanding the theory of semantic search to constructing your first functional RAG pipeline. You will learn how to transform raw text into vector embeddings, query a vector database, and construct precise prompts that yield highly accurate answers. What you'll learn: 1. Understand the core architecture of RAG and how it differs from fine-tuning. 2. Convert unstructured text into vector embeddings using modern embedding models. 3. Store and query semantic data using popular open-source vector databases. 4. Apply prompt engineering techniques to combine retrieved context with user queries. 5. Evaluate and optimize the accuracy of your system's responses. 6. Address common LLM limitations like hallucination and outdated knowledge. Starting with essential AI and database terminology, you will progress through step-by-step written explanations and code snippets that demonstrate how to connect data loaders, embedding generators, and language models. This course is designed for software developers, data enthusiasts, and technical product managers who are new to AI engineering. No prior experience with machine learning is required, though basic Python familiarity is helpful. Start reading today to unlock the power of context-aware AI applications.