How to Fix ANY Prompt With Meta Prompting
Mark Kashef
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I'm an AI expert (and mad scientist) with over 10 years in Data Science & NLP I've been running my AI Automation Agency, Prompt Advisers, for the past 2 years
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Video Description
🚀 Gumroad Link to Assets in Video: https://bit.ly/3Buzev8 👉🏼Join the Early AI-dopters Community: https://bit.ly/3ZMWJIb 🌐 Visit My Agency Website: https://bit.ly/4cD9jhG 📅 Book a Paid Consultation: https://bit.ly/3Ml5AKW 🎬 Core Video Description Imagine finally having the tools to make your AI prompts work flawlessly. If you've been frustrated with your prompts not delivering the desired results, this video is for you! In this video, I introduce the concept of metaprompting and show you how to use AI to audit and improve any prompt you have. By the end of this video, you'll see how metaprompting can turn generic prompts into highly effective ones by leveraging AI's feedback. We'll also discuss the strengths and weaknesses of different large language models like Claude, GPT-4 Omni, and others, and how to use them effectively. This video is a must-watch for AI enthusiasts, business strategists, and developers looking to enhance their workflow with advanced AI prompting techniques. 👋 About Me: Hello! I'm Mark, a seasoned Data Science Manager by day and an AI automation agency owner by night, hailing from Canada with a decade in the AI space. At Prompt Advisers, we specialize in cutting-edge AI solutions, helping individuals, businesses, and agencies fully harness applied AI. Having been featured in interviews and recognized for our innovative contributions, we're dedicated to guiding you through the AI landscape. TIMESTAMPS ⏳ 0:00 - Introduction: Meta prompting to audit prompts. 0:05 - Mark’s AI Agency background and meta prompting overview. 0:32 - Three reasons why a prompt might not work. 0:49 - Example: LLMs perform differently based on tasks. 1:17 - Chain prompting: Breaking down complex tasks. 2:08 - Generating a vanilla prompt and setting up feedback. 2:51 - Fictional story: Tortoise and hare example. 3:46 - Feedback: Improving tone and style of the story. 5:22 - Outcome: Modernized tortoise and hare in a 2024 setting. 5:55 - Cold email generation: AI banana peeler product. 6:58 - Feedback: Making the email more clickbait and engaging. 8:03 - Outcome: Improved cold email with a more engaging tone. 8:45 - Feedback iteration: Shortening and refining the email further. 9:43 - Restaurant review generation for a pizza place. 10:15 - Feedback: Make the review more succinct. 10:46 - Outcome: A shorter, punchier restaurant review. 11:10 - Job description: Software engineer position. 11:50 - Feedback: Add a secret message to catch AI-generated resumes. 12:52 - Job description with secret message added. 13:55 - Testing the prompt on HR manager job description. 14:36 - Wedding invitation creation: English, German, and Spanish versions. 15:06 - Feedback: Adding multilingual sentences to the invitation. 15:40 - Final example: Multilingual wedding invitation. 16:00 - Closing thoughts on meta prompting. 16:05 - Conclusion: Final meta prompting video for now, feedback request, and subscribe reminder. #Metaprompting #PromptAuditing #AIFrameworks #BusinessStrategy #TechTutorial #ArtificialIntelligence #PromptEngineering #ClaudeAI #AIAgents #BusinessOptimization #AIResponses #FrameworkPrompting #strategicthinking
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