Build AI Workflows with LangChain and Open-Source LLMs

As artificial intelligence continues to evolve, developers are increasingly turning to open-source language models to build powerful, privacy-focused applications. If you want to create custom AI workflows without depending on expensive proprietary APIs, mastering LangChain is the perfect next step. This text-based course will guide you through the process of connecting open-source large language models (LLMs) to real-world data. You will start with the fundamental terminology of generative AI and LangChain components, gradually moving toward building robust pipelines. By reading through clear explanations and practical code snippets, you will learn how to structure prompts, manage memory, and implement modern retrieval techniques to make your AI applications smarter and more context-aware. What you will learn: * Understand the core architecture and key terminology of the LangChain framework. * Configure and integrate open-source large language models into your local environment. * Apply fundamental prompt engineering techniques to guide model responses effectively. * Build Retrieval-Augmented Generation (RAG) pipelines using basic vector database concepts. * Create sequential chains to automate multi-step artificial intelligence tasks. * Practice handling application memory to maintain conversational context. The curriculum flows logically from basic definitions and setup to constructing multi-stage AI pipelines using text-based exercises. You will explore modern development patterns, ensuring your skills align with current industry standards for building LLM applications. This course is designed for beginners to AI development who have a basic understanding of Python programming but are completely new to LangChain and generative AI workflows. Start reading today to unlock the potential of open-source AI and build your first intelligent application.