Building Generative AI and LLM Applications with Python

Generative AI is reshaping how we build software, but moving from simple chat prompts to robust Python applications requires a structured approach. This course guides you through the foundational concepts of Large Language Models (LLMs) and shows you how to programmatically control them. You will transition from a curious developer to an AI practitioner capable of architecting intelligent applications. You will read clear explanations of key AI concepts, study clean Python code patterns, and learn how to connect language models to external data sources. What you'll learn: Understand core LLM concepts, tokenization, and how generative models predict text; Design effective system prompts and implement structured outputs using Python; Connect applications to external data using retrieval-augmented generation (RAG) and vector databases; Manage API integrations safely using modern Python environment variables and error-handling patterns; Build conversational agents that can execute basic tools and maintain chat history. The course begins with essential AI definitions and model architectures before moving into API orchestration and hands-on Python implementation. You will progress systematically from basic API calls to designing modular, data-aware AI workflows. This text-based guide is designed for beginners and intermediate Python developers looking to enter the AI space, with no prior machine learning experience required. Start reading today to unlock the potential of generative AI in your own software projects.