How to Use Reflexion Prompting in ChatGPT & Python: Step-by-Step Tutorial

🧠 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-automations-4579 Ready to make your AI models smarter and more self-aware? In this tutorial, you'll learn how to implement Reflexion Prompting using ChatGPT and Python—a powerful technique where language models reflect on their mistakes, revise answers, and improve over time. Perfect for AI developers, prompt engineers, and researchers! Code: https://ryanandmattdatascience.com/reflexion-prompting/ 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/data-freelancing/ 👨‍💻 Mentorships: https://ryanandmattdatascience.com/mentorship/ 📧 Email: [email protected] 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord: https://discord.com/invite/F7dxbvHUhg 📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT OpenAI/Langchain Playlist: https://www.youtube.com/watch?v=5qP6u-WGSPk&list=PLcQVY5V2UY4Kat6vxC7ESzIIzHWdwlnak&ab_channel=RyanNolanData DSP Prompting: https://youtu.be/WEAaVKVfy3M React Prompting: https://youtu.be/EcB0PiNmbFo Chain of Thought Prompting: https://youtu.be/RL0cmE0dAF0 In this video, I walk through a powerful prompting technique called reflection (or reflection-X) that significantly improves AI responses. This technique is especially useful for chatbots, problem-solving tasks like math questions, and reducing hallucinations in large language model outputs. I explain how reflection prompting works by asking the AI to reflect on its initial response before providing a final answer. Instead of accepting the first output, you prompt the model to evaluate its own work, identify strengths and weaknesses, and then generate an improved version. I demonstrate two practical examples: one using ChatGPT with a prompt about Tampa Bay Rays baseball history, and another using Python with LangChain to explain Einstein's E=mc² equation to a five-year-old. The Python implementation shows how to build three prompt templates (initial, reflection, and improved response) and chain them together using LangChain. By the end of this tutorial, you'll understand exactly how to implement reflection prompting in your own projects to get higher quality, more accurate responses from AI systems. Whether you're building chatbots or just want better AI outputs, this technique is a game-changer. TIMESTAMPS 00:00 Introduction to Reflection Prompting 01:25 Chatbot Example Walkthrough 03:02 ChatGPT Demonstration - Tampa Bay Rays 05:17 Evaluating and Improving the Response 06:24 Python Setup with LangChain 08:15 Defining the Language Model 09:21 Creating Prompt Templates 12:01 Building Chains and User Prompts 15:35 Running Initial Response 16:53 Reflection Analysis 18:34 Final Improved Response 20:09 Code Summary and Recap OTHER SOCIALS: Ryan’s LinkedIn: https://www.linkedin.com/in/ryan-p-nolan/ Matt’s LinkedIn: https://www.linkedin.com/in/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.