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

Conversational AI is transforming how we interact with technology, but building custom solutions requires understanding how to orchestrate language models effectively. This written course guides you through the process of creating your own intelligent chatbots from scratch using modern open-source tools. You will transition from understanding basic natural language processing concepts to building functional, context-aware AI assistants. You will gain a deep understanding of how large language models work, how to connect them to external data sources, and how to deploy clean user interfaces. What you will learn: - Understand the fundamental mechanics of transformer models and natural language processing. - Apply open-source large language models to tasks like summarization, translation, and reasoning. - Configure Retrieval-Augmented Generation (RAG) pipelines using LangChain and vector databases. - Design effective system prompts and apply prompt engineering techniques to guide model behavior. - Build interactive web interfaces for your AI applications using Streamlit. - Evaluate and optimize chatbot performance for real-world deployment. The course starts with essential definitions and foundational AI concepts before guiding you through data preparation, retrieval workflows, and interface design. You will read structured explanations and analyze practical code snippets that demonstrate how to connect LLMs to your data. This course is designed for beginner developers, data enthusiasts, and curious creators who want to learn AI development; no prior experience with machine learning is required. Start reading today to build your own custom AI conversational assistants.