Prompting Large Language Models in Python

Integrating generative AI into Python scripts enables automated text analysis, dynamic workflows, and smart conversational agents, but obtaining reliable and consistent results requires disciplined prompt engineering. This text-based course guides you through the foundational concepts of working with large language models programmatically, showing you how to craft robust prompts and manage API workflows with confidence. You will begin with essential terminology, learning how models process tokens, how parameters like temperature influence output, and how modern chat completion endpoints operate. From there, you will explore structured prompting techniques, conversation memory handling, and output parsing strategies. What you'll learn: - Understand core LLM architecture concepts, context limits, and token mechanics - Construct structured system and user prompts using few-shot techniques - Manage multi-turn conversation history and message state programmatically - Enforce structured outputs such as JSON for dependable data extraction - Implement error handling, retries, and token optimization in Python scripts - Apply modern tool calling and retrieval-augmented generation concepts The written material starts with core foundational definitions before guiding you through clear Python code snippets and practical prompt development patterns. This course is designed for beginner Python programmers and developers who want to integrate AI capabilities into their projects without needing prior machine learning experience. Begin reading to start building reliable AI-driven Python applications.