Building LLM Applications in Python

The demand for intelligent, AI-driven software is growing rapidly, but integrating Large Language Models (LLMs) into real-world applications requires more than just sending a text prompt. This course takes you step-by-step through the process of building robust LLM applications using Python. You will start with the foundational concepts of AI integration and move toward practical implementation, learning how to structure your code, manage prompts, and apply modern techniques like Retrieval-Augmented Generation (RAG) to create context-aware applications. What you'll learn: • Understand the core architecture of LLM-powered applications and key terminology. • Connect to and interact with language model APIs using Python. • Apply prompt engineering best practices to generate accurate, reliable outputs. • Build Retrieval-Augmented Generation (RAG) pipelines using vector databases. • Manage Python environments and dependencies with modern tools like uv or Poetry. • Prepare your application for deployment using foundational MLOps principles. The curriculum begins with essential definitions and API basics before progressing into hands-on written exercises. You will read through clear explanations and practical code snippets to gradually build your understanding of the complete application lifecycle. Designed for beginners and developers new to the AI landscape, this course requires no prior machine learning experience—just a basic understanding of Python. Start reading today and take your first steps into the world of AI application development.