Unlock the Power of Chain of Thought Prompting for Python & ChatGPT

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-aut... Want smarter, more accurate responses from ChatGPT? Discover how Chain of Thought (CoT) prompting can transform your AI workflows! In this video, we break down how to implement CoT with Python and ChatGPT to get more human-like reasoning and deeper insights from your prompts. Code: https://ryannolandata.com/chain-of-th... 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/da... 👨‍💻 Mentorships: https://ryanandmattdatascience.com/me... 📧 Email: [email protected] 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord:   / discord   📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT OpenAI/Langchain Playlist:    • How to Build Your First AI LLM Prompts wit...   Zero, One, and Few Shot Prompting:    • Zero, One, and Few Shot Prompting with Lan...   Output Parsers:    • Langchain Output Parsers Simplified   Chat with a CSV:    • Chat with a CSV - LangChain CSV Agents Tut...   In this comprehensive video, I break down Chain of Thought (CoT) prompting and show you exactly how to implement it using both ChatGPT and the OpenAI API with Python. Chain of Thought prompting is a powerful technique that helps large language models show their reasoning step-by-step, rather than just providing a final answer. This leads to more accurate results and better transparency in how AI arrives at solutions. I walk through four main approaches: zero-shot CoT prompts where you simply ask the model to show its work, one-shot prompts where you provide a single example, few-shot prompts with multiple examples, and automatic Chain of Thought (Auto-CoT) that breaks down complex problems into smaller questions. For each method, I demonstrate practical examples with both ChatGPT and Python code using Google Colab, making it accessible whether you're a coder or not. The video also covers advanced multimodal examples using GPT-4o, showing how to apply Chain of Thought reasoning to images like graphs and geometric shapes. By the end, you'll understand exactly when and how to use each type of CoT prompt to get the best results from your AI systems, with all code examples available on my website for easy implementation. TIMESTAMPS 00:00 What is Chain of Thought Prompting? 01:26 Video Overview & Setup 03:02 Zero-Shot CoT Example (ChatGPT) 05:17 Zero-Shot CoT Example (Python) 08:15 One-Shot CoT Example (ChatGPT) 10:05 One-Shot CoT Example (Python) 13:32 Building Proper One-Shot Prompt Templates 16:47 Few-Shot CoT Example (ChatGPT) 18:28 Few-Shot CoT Example (Python) 21:00 Automatic Chain of Thought (Auto-CoT) 24:52 Multimodal CoT Example (ChatGPT) 26:22 Multimodal CoT Example (Python with GPT-4o) OTHER SOCIALS: Ryan’s LinkedIn:   / ryan-p-nolan   Matt’s LinkedIn:   / matt-payne-ceo   Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.