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

Organizations and developers increasingly need to build custom AI solutions that are secure, cost-effective, and independent of proprietary APIs. This text-based course guides you through the process of creating your own intelligent conversational agents using entirely open-source tools. By reading through clear explanations and studying practical code implementations, you will transition from understanding basic language model concepts to building fully functional, interactive chatbot applications. You will gain a solid grasp of how to connect LLMs to your own data sources for accurate, context-aware responses. What you'll learn: Understand the foundational concepts of Large Language Models and transformer architectures; Configure open-source LLMs for specialized tasks like text summarization and reasoning; Build Retrieval-Augmented Generation (RAG) workflows using LangChain and vector databases; Design clean, interactive web interfaces for your chatbots using Streamlit; Apply advanced prompt engineering techniques to control and refine chatbot behavior; Evaluate chatbot performance and understand modern deployment strategies. This course begins with essential AI terminology and basic language model mechanics before guiding you step-by-step through LangChain integration and Streamlit interface design. This course is designed for beginner developers, software enthusiasts, and tech professionals looking to enter the world of conversational AI. No prior background in machine learning is required, though a basic familiarity with Python is recommended. Start reading today to master the fundamentals of modern open-source AI development.