Building Generative AI Applications with Python and LangChain

Generative artificial intelligence is transforming how we build software and interact with data. This course guides you through the core concepts of large language models (LLMs) and teaches you how to build your own AI-powered solutions from scratch. You will progress from understanding foundational AI terminology to writing clean Python code that integrates LLMs, vector databases, and external data sources. By studying clear written explanations and practical code examples, you will gain the confidence to design, test, and optimize custom AI assistants and automation tools. What you'll learn: - Understand the core architecture of transformers, large language models, and modern generative AI systems. - Apply advanced prompt engineering techniques to guide model behavior and optimize text outputs. - Build retrieval-augmented generation (RAG) pipelines to connect LLMs with custom external data sources. - Configure vector databases to store and retrieve high-dimensional embeddings efficiently. - Implement orchestration frameworks like LangChain to manage complex, multi-step AI workflows. - Evaluate model performance and explore foundational concepts of fine-tuning and model customization. The course begins with essential terminology and the mechanics of language models before moving into hands-on Python implementations, vector storage, and RAG architectures. You will learn through structured text explanations and step-by-step code walkthroughs designed for self-paced reading. This course is designed for beginners, aspiring AI developers, and software enthusiasts looking to enter the world of generative AI. No prior experience with artificial intelligence is required, though a basic familiarity with Python programming is helpful. Start reading today to build your first intelligent language application.