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

AI chatbots are transforming how we interact with technology, but building them does not require expensive proprietary APIs. By leveraging open-source large language models (LLMs) and modern orchestration libraries, you can build custom, secure, and highly intelligent conversational agents. This text-based course guides you from foundational AI concepts to deploying your own functional chatbot interface. You will learn the mechanics behind modern language models, understand how to connect them to external data sources, and build interactive web interfaces using clean, readable Python code. What you'll learn: 1. Understand the foundational architecture of transformers, large language models, and natural language processing. 2. Apply prompt engineering techniques to guide chatbot behavior, tone, and accuracy. 3. Configure Retrieval-Augmented Generation (RAG) pipelines using LangChain to connect LLMs to custom documents. 4. Implement vector databases to store and retrieve semantic information efficiently. 5. Build web-based user interfaces for your chatbots using Streamlit. 6. Evaluate and optimize chatbot responses for reliability, context relevance, and performance. You will start with core definitions and the theory of language models before diving into step-by-step written implementation guides, code snippets, and practical integration exercises. This course is designed for beginner developers and tech enthusiasts with basic Python knowledge; no prior background in machine learning or AI is required. Start reading today to construct your own custom AI conversational applications from scratch.