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

Creating intelligent conversational agents is no longer restricted to organizations with massive budgets. With modern open-source tools, developers and tech enthusiasts can build custom, secure, and highly capable AI chatbots from scratch. This text-based course guides you through the foundational concepts and practical steps to design, develop, and deploy your own AI-powered chat applications. You will transition from understanding basic language model mechanics to structuring complex retrieval workflows and building clean web interfaces. What you'll learn: Understand the fundamental mechanics of Large Language Models (LLMs) and transformer architectures; Configure LangChain to manage prompts, chains, and conversational memory; Implement Retrieval-Augmented Generation (RAG) patterns to connect your chatbot to custom documents; Utilize vector databases to store and retrieve semantic information efficiently; Build interactive web-based chat interfaces using Streamlit; Apply modern prompt engineering techniques to optimize response accuracy and safety. You will start by exploring core generative AI terminology and foundational concepts before diving into step-by-step code explanations. The material progresses logically from single-prompt interactions to fully integrated, document-aware conversational applications. This course is designed for beginner developers, technical writers, and AI enthusiasts who want to enter the world of conversational AI. No prior experience with LLMs or machine learning is required, though a basic familiarity with Python is helpful. Start reading today to unlock the potential of open-source conversational AI.