Zero, One, and Few Shot Prompting with Langchain and OpenAI LLMs скачать в хорошем качестве

Zero, One, and Few Shot Prompting with Langchain and OpenAI LLMs

🧠 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... In this python coding video we delve into the realms of zero-shot, one-shot, and few-shot prompting using the powerful combination of Langchain and OpenAI with large language models 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/da... 👨‍💻 Mentorships: https://ryanandmattdatascience.com/me... 📧 Email: ryannolandata@gmail.com 🌐 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...   Chain of Thought Prompts:    • Unlock the Power of Chain of Thought Promp...   Langchain Selectors:    • Unlocking the Power of Selectors in LangCh...   GPT 4 Vision for Beginners:    • GPT 4 Vision (PYTHON) Tutorial for Beginners   In this video, I break down few-shot prompts and show you exactly how to build templates for your large language models that dramatically improve performance. Few-shot prompting is a critical technique where you provide multiple examples to your AI model, defining both the input and output format to get consistent, high-quality results every time. We start with zero-shot prompts (no examples) and show why they produce inconsistent outputs. Then we move to one-shot prompts with a single example, and finally build a complete few-shot prompt system using 2-5 examples. I walk through real code examples using Python, OpenAI, and LangChain, showing you how to set up prompt templates, format multiple examples, and get reliable results from your AI models. You'll see practical examples using baseball statistics to demonstrate how few-shot prompts handle complex formatting, multiple data points, and variable inputs. By providing clear examples upfront, you teach the model exactly what output format you expect, whether that's structured data, specific formatting, or handling edge cases like players who played for multiple teams. By the end of this tutorial, you'll know how to implement zero-shot, one-shot, and few-shot prompts in your own AI projects, understand when to use each approach, and be able to build robust prompt templates that consistently deliver the results you need. TIMESTAMPS 00:00 Introduction to Few-Shot Prompts 01:05 Setting Up Python Environment 02:30 Creating OpenAI API Key Variable 03:30 Zero-Shot Example Introduction 05:25 Running Zero-Shot Prompts 06:45 One-Shot Example Setup 08:40 Building Example Prompt Template 10:25 Testing One-Shot Results 12:45 Few-Shot Examples Introduction 14:30 Adding Multiple Player Examples 16:30 Creating Few-Shot Prompt Template 18:30 Testing Few-Shot Results 19:45 Recap and Conclusion 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.