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

Natural language processing is transforming how we interact with technology, and building custom conversational agents is now more accessible than ever. This text-based course shows you how to harness the power of open-source artificial intelligence to create your own intelligent chatbots. You will transition from understanding basic language model concepts to constructing fully functional, interactive conversational applications. By working through clear written explanations and practical code snippets, you will master the mechanics of prompt engineering, information retrieval, and user interface design. What you'll learn: - Understand the foundational mechanics of Transformers and Large Language Models (LLMs). - Implement Retrieval-Augmented Generation (RAG) to connect chatbots to custom data sources. - Configure vector databases to store and retrieve contextual information efficiently. - Build interactive web interfaces for your AI applications using Streamlit. - Manage chatbot memory and multi-step reasoning chains with LangChain. - Apply modern prompt engineering techniques to improve model accuracy and response quality. The course begins with core definitions and the theoretical underpinnings of natural language processing before moving into practical implementation. You will progress from writing basic model queries to assembling complete retrieval chains and launching a clean, responsive user interface. This course is designed for aspiring developers, data enthusiasts, and tech-savvy beginners who want to build AI applications. No prior experience with machine learning is required, though a basic familiarity with Python is helpful. Start reading today to build your first intelligent conversational agent from scratch.