Building AI Chatbots with Open-Source LLMs, LangChain, and Streamlit

Organizations and developers are rapidly adopting conversational AI to automate tasks and improve user experiences, but building these systems requires a solid understanding of modern tools. This text-based course guides you through the foundational concepts and practical steps needed to create intelligent, responsive chatbots from scratch. You will transition from understanding basic natural language processing to constructing fully functional, interactive conversational agents. By learning how to connect open-source Large Language Models (LLMs) with external data sources, you will build systems that provide accurate, context-aware answers. What you will learn: Understand foundational AI concepts, transformer architectures, and how open-source LLMs process text. Apply prompt engineering techniques to guide model behavior and improve response quality. Implement Retrieval-Augmented Generation (RAG) using LangChain to connect chatbots to custom documents. Configure vector databases to store and retrieve semantic information efficiently. Create clean, interactive user interfaces for your AI applications using Streamlit. Evaluate chatbot performance and understand modern deployment workflows. The course begins with essential terminology and the mechanics of language models before moving into hands-on integration with LangChain. You will then progress to building user interfaces and testing your application's performance. This course is designed for beginners, software developers, and tech enthusiasts who want to enter the field of AI engineering. No prior experience with LLMs or machine learning is required, though a basic familiarity with Python is helpful. Start reading today to build your own custom, private AI chat applications.