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Prompt Engineering With Python & Openai (chatgpt) Api
Last updated 4/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.34 GB| Duration: 5h 51m
Build Production AI Apps With Python - Master Prompting, Function Calling, Error Handling With 25+ Hands-On Notebooks[/center] 
What you'll learn
Connect Python to OpenAI's ChatGPT API and generate structured outputs
Write Python functions that integrate multiple live APIs (NewsAPI, Google Trends, Federal Reserve data)
Parse and analyze JSON responses to extract meaningful insights
Automate news sentiment classification using AI-powered text analysis
Visualize sentiment and trend data with simple Python charts and dashboards
Combine multiple data sources into a unified scoring framework for company or industry analysis
Apply error handling and rate limiting to create reliable API workflows
Adapt the same pipeline patterns to competitor tracking, brand monitoring, and regulatory intelligence projects
Requirements
Basic Python familiarity: classes, functions, loops, and importing libraries
Ability to read simple JSON data and error messages
No prior Jupyter Notebook experience (setup and usage explained in the course)
No advanced math, finance, or machine learning background needed
Description
The API is where real applications are built. This course is how you get there.
This course is for Python developers who are ready to move from experimenting with AI to actually building with it. You'll work directly with OpenAI's modern Responses API - the one OpenAI recommends for all new projects - writing real code that connects to real tools and produces real results.
WHAT YOU'LL BUILD
You'll complete three capstone projects, each one closing out a module after the concepts that make it possible have been taught.
Research Assistant - Decomposes complex questions into sub-questions, investigates each one independently, and synthesizes the findings into a structured answer. Built using instruction chaining, personas, and advanced few-shot techniques.
Production Support Bot - A fully functional support chatbot with budget controls, sliding window context management, and response caching. Built incrementally across two modules to show how production systems are actually assembled - not just demonstrated in a single notebook.
Multi-Tool Agent - Connects to a live weather API and queries a real SQLite database using function calling. This is AI that interacts with the outside world through Python functions.
WHAT YOU'LL LEARN
API Fundamentals - Connect to the OpenAI API, configure your environment, and make your first calls using the Responses API. Understand model selection, token usage, and cost tracking from day one.
Core Prompting - Zero-shot, one-shot, and few-shot prompting. Understand exactly how the model responds to different prompt structures and w***t matters.
Production Prompting - Structured JSON outputs for reliable parsing, error handling with exponential backoff, reusable prompt templates, and systematic prompt evaluation so you can measure whether your prompts are actually working.
Advanced Prompting - Instruction chaining, role-based personas, advanced few-shot techniques, and self-consistency strategies for more reliable outputs.
Production Patterns - Token counting and cost tracking with tiktoken, context window strategies for long conversations, and response caching to eliminate redundant API calls.
Function Calling - The complete function calling workflow. Connect the AI to external tools, live APIs, and real databases so it can take actions in the world.
HOW THE COURSE IS STRUCTURED
Six modules. 25+ hands-on Jupyter notebooks. Each concept is taught in its own notebook with working code you can run, modify, and reuse. Each module closes with a capstone that puts everything you just learned into a real, deployable application.
PREREQUISITES
Basic Python familiarity - classes, functions, loops, and importing packages. Environment setup is covered in Module 1.
You'll also need an OpenAI account with a minimum of $5 in API credit. That's more than enough to complete every exercise in the course using gpt-5-mini, the default model used throughout.
WHO THIS COURSE IS FOR
Engineers adding AI capabilities to existing applications. Analysts automating workflows with Python. Technical leads evaluating how to integrate AI into their teams' work.
WHO THIS COURSE IS NOT FOR
Complete beginners to Python. If you're new to Python, build that foundation first - you'll get significantly more out of this course when you come back.
Who this course is for
Python developers who want to build real applications using the OpenAI API
Engineers adding AI capabilities to existing products or workflows
Technical leads evaluating how to integrate AI into their teams' projects