Python LLM Application Development: From Concept to Production

As Large Language Models transform software engineering, building production-ready AI tools requires going beyond simple prompt calls. This course guides you through the full lifecycle of designing, building, and deploying AI products using Python and modern developer tools. You will acquire a grounded understanding of LLM architecture, context retrieval, API backends, and system monitoring through clear written explanations and code exercises. What you will learn: Understand core LLM concepts, API integrations, and prompt execution fundamentals. Build Retrieval-Augmented Generation systems using vector databases like Qdrant. Create complex multi-step agent workflows using LangChain and graph orchestration patterns. Develop robust application backends with FastAPI and persistent PostgreSQL storage. Monitor, trace, and evaluate model responses for production reliability. Package and deploy scalable AI microservices using Docker containers. The course begins with foundational terminology, basic prompt execution, and model connectivity before advancing to data pipelines, state management, and backend deployment. This program is tailored for Python developers and curious technical learners who want to construct real-world AI applications from scratch without needing a complex machine learning background. Take the first step today toward building reliable AI-powered Python applications.