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

Conversational AI is transforming how we interact with technology, but building custom, secure chatbots requires understanding how to connect language models with your own data. This course guides you through the entire process of creating intelligent, responsive chat interfaces from scratch. You will transition from understanding basic artificial intelligence concepts to constructing fully functional, data-aware chatbot applications. Through clear, step-by-step written explanations and practical code examples, you will learn how to leverage open-source Large Language Models (LLMs), orchestrate complex workflows, and build clean user interfaces. What you'll learn: - Understand the foundational architecture of transformers and how Large Language Models process text - Configure open-source LLMs for tasks like summarization, translation, and structured reasoning - Apply Retrieval-Augmented Generation (RAG) to connect your chatbot to custom document databases - Design effective prompts and manage conversation memory using the LangChain framework - Build web-based chat interfaces using Streamlit to interact with your models in real time - Evaluate chatbot performance and understand modern deployment best practices The course begins with core terminology, foundational AI concepts, and natural language processing basics. You will then progress through data preparation, vector databases, retrieval chains, and interface development, culminating in a complete conversational application. This course is designed for beginners interested in AI development, software developers expanding their toolkit, and tech enthusiasts. No prior experience with LLMs or machine learning is required, though basic Python familiarity is helpful. Start reading today to build your own custom AI conversational agents.