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

Artificial intelligence is transforming how we interact with technology, and building custom chatbots is now more accessible than ever. By leveraging open-source large language models (LLMs), you can create powerful, private, and customizable conversational applications without relying on expensive proprietary APIs. In this course, you will transition from understanding the core mechanics of natural language processing to building and deploying your own interactive AI chatbots. You will gain practical experience in structuring conversational flows, managing context, and connecting your models to external data sources. What you'll learn: Understand foundational LLM concepts, transformer architectures, and natural language processing terminology; Implement Retrieval-Augmented Generation (RAG) to connect chatbots to custom documents and vector databases; Manage conversational memory and complex workflows using the LangChain framework; Design interactive web-based user interfaces for your AI applications using Streamlit; Apply prompt engineering techniques to guide model behavior and optimize response quality; Evaluate and deploy your conversational agents for real-world interactions. This written course starts with essential definitions and core concepts of generative AI before guiding you step-by-step through practical implementation. You will explore code examples that demonstrate how to link open-source models, manage application state, and launch a complete chatbot interface. This course is designed for aspiring developers, data enthusiasts, and tech-savvy professionals who want a clear, beginner-friendly introduction to modern AI development without needing prior machine learning experience. Start reading today and build your first intelligent conversational application from scratch.