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

Chatbots are transforming how we interact with technology, but building one that is truly intelligent, context-aware, and responsive requires the right foundation. This text-based course guides you through the process of creating your own conversational AI applications using modern open-source tools. You will transition from understanding basic natural language concepts to building and evaluating fully functional, retrieval-augmented AI chatbots. Through clear written explanations and structured code walkthroughs, you will gain the skills needed to connect large language models to custom data sources and present them through clean user interfaces. What you'll learn: - Understand the foundational architecture of transformer models and natural language processing. - Apply open-source large language models to text summarization, translation, and reasoning tasks. - Build Retrieval-Augmented Generation (RAG) pipelines using LangChain and vector database concepts. - Design and refine effective prompts to optimize chatbot behavior and output quality. - Create interactive, web-based user interfaces for your AI applications using Streamlit. - Evaluate chatbot performance and understand modern deployment best practices. The course begins with core AI and language model concepts before guiding you through data preparation, vector search, and conversational chains. You will then learn to tie everything together in a web interface and evaluate the system's performance. This course is designed for aspiring AI developers, software engineers, and tech enthusiasts who want to build conversational applications. No prior experience with LLMs or LangChain is required, though basic Python familiarity is helpful. Start building your own intelligent, data-driven chatbots today.