Retrieval-Augmented Generation (RAG) Fundamentals with LangChainGo

Large language models are powerful, but they often lack access to your specific, real-time data or suffer from hallucinations. Retrieval-Augmented Generation (RAG) solves this by connecting LLMs to external knowledge bases, ensuring highly accurate and context-aware responses. By taking this written course, you will transition from understanding basic AI limitations to structuring clean, production-ready RAG pipelines using Go and the LangChainGo framework. You will learn how to leverage vector databases for semantic search and feed relevant context directly to your models. What you'll learn: - Understand the core architecture of Retrieval-Augmented Generation and how it addresses LLM limitations. - Configure vector databases to store, index, and query document embeddings efficiently. - Implement document loading, text splitting, and embedding generation workflows using Go. - Apply semantic search techniques to retrieve the most relevant context for user queries. - Construct prompt templates that safely integrate retrieved data with language models. - Explore modern RAG optimization patterns including hybrid search and basic evaluation concepts. We begin with essential AI and database terminology before walking you through step-by-step Go code implementations and architectural patterns. You will read through clear explanations and analyze structured code examples designed to build your practical confidence. This course is designed for Go developers and software engineers new to AI orchestration, requiring only a basic familiarity with Go syntax and no prior machine learning experience. Start reading today to build smarter, data-driven AI systems using Go.