Building Chat Applications with LangChainGo and Gemini

Go is renowned for its performance and concurrency, making it an excellent choice for building scalable backend services. By combining Go with LangChainGo and modern LLM APIs like Gemini, you can build production-ready, AI-driven chat applications. In this text-based course, you will transition from basic Go programming to developing intelligent conversational agents. You will learn how to integrate large language models, manage stateful user interactions, and implement retrieval-augmented generation (RAG) to query custom documents. What you'll learn: - Understand the core principles of LangChainGo and how it simplifies LLM integration in Go - Connect to the Gemini API and configure model parameters for optimal chat responses - Manage conversation history and memory to maintain context across multiple chat turns - Implement document-based Q&A using retrieval-augmented generation patterns - Apply Go concurrency patterns and context propagation to handle API calls efficiently - Structure your Go code cleanly using environment variables and modern configuration standards The course starts with fundamental concepts of generative AI and LangChainGo architecture before guiding you through step-by-step code implementations. You will read detailed explanations, analyze structured code snippets, and complete written exercises to solidify your understanding of AI application development in Go. This course is designed for Go developers who want to enter the AI space. No prior experience with machine learning or AI APIs is required, though a basic understanding of Go syntax is recommended. Start building highly performant, AI-powered Go applications today.