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-aut...
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/re...
🚀 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...
DSP Prompting: • How to Use Directional Stimulus Prompting ...
React Prompting: • ReAct Prompting in Langchain: Python Tutor...
Chain of Thought Prompting: • Unlock the Power of Chain of Thought Promp...
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: / 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.