LLM Application Engineering with Python and LangChain

Generative AI is transforming how we build software, but moving from simple prompts to production-ready AI applications requires a structured engineering approach. This text-based course guides you through the foundational concepts and practical patterns needed to build smart, context-aware systems. You will transition from writing basic scripts to designing robust, agentic workflows. By reading step-by-step explanations and analyzing structured code examples, you will understand how to connect large language models to external data sources, manage conversational memory, and deploy your solutions as production-ready APIs. What you'll learn: Understand the core architecture of large language models and prompt engineering fundamentals; Build retrieval-augmented generation (RAG) pipelines to connect LLMs with vector databases; Implement conversational memory and state management using LangChain and LangGraph; Design autonomous AI agents capable of tool use and multi-step decision-making; Apply modern Python practices including async/await and type hints for efficient API development; Configure production-ready workflows with caching, logging, and performance monitoring. The course starts with essential AI terminology and foundational prompt techniques before moving into hands-on pipeline construction, agent design, and final deployment strategies. It is designed for Python developers and tech enthusiasts looking to enter the AI engineering space, with no prior machine learning experience required. Start reading today to master the essentials of modern LLM application development.