Noah Hein, from Latent Space University, is launching a free lightning course on AI Engineering, while Sander Schulhoff helped create The Prompt Report, a definitive guide to prompt engineering. They discuss the vast landscape of over 1,600 scholarly papers on prompting techniques, the evolution of prompt engineering into a core skill for AI engineers, and ethical implications of AI-generated academic content. With insights into optimizing AI models and real-world applications of prompting strategies, this conversation is a treasure trove for AI enthusiasts.

Sander Schulhoff from LearnPrompting.org discusses prompt techniques for large language models, including few-shot prompting and AI trends. They explore AI course expansion, data pulling pipelines, and advanced prompting techniques. Challenges in structuring prompts, model training, agent prompting, and prompt optimization are also covered.

In this episode, Nathan sits down with Sander Schulhoff, Cofounder and CEO of Learnprompting.org. They discuss the business model, the keys to prompting that every user of language models should know, negative prompting, prompt hacking, and more. If you need an ecommerce platform, check out our sponsor Shopify: https://shopify.com/cognitive for a $1/month trial period.LINKS:- Learnprompting.org: https://learnprompting.org/- Learnprompting.org Prompt Hacking: https://learnprompting.org/docs/category/-prompt-hacking- Ignore This Title and Hackaprompt: https://arxiv.org/abs/2311.16119SPONSORS:The Brave search API can be used to assemble a data set to train your AI models and help with retrieval augmentation at the time of inference. All while remaining affordable with developer first pricing, integrating the Brave search API into your workflow translates to more ethical data sourcing and more human representative data sets. Try the Brave search API for free for up to 2000 queries per month at https://brave.com/apiShopify is the global commerce platform that helps you sell at every stage of your business. Shopify powers 10% of ALL eCommerce in the US. And Shopify's the global force behind Allbirds, Rothy's, and Brooklinen, and 1,000,000s of other entrepreneurs across 175 countries.From their all-in-one e-commerce platform, to their in-person POS system – wherever and whatever you're selling, Shopify's got you covered. With free Shopify Magic, sell more with less effort by whipping up captivating content that converts – from blog posts to product descriptions using AI. Sign up for $1/month trial period: https://shopify.com/cognitiveOmneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off www.omneky.comNetSuite has 25 years of providing financial software for all your business needs. More than 36,000 businesses have already upgraded to NetSuite by Oracle, gaining visibility and control over their financials, inventory, HR, eCommerce, and more. If you're looking for an ERP platform ✅ head to NetSuite: http://netsuite.com/cognitive and download your own customized KPI checklist.X/SOCIALS:@labenz (Nathan)@SanderSchulhoff@learnprompting@CogRev_PodcastTIMESTAMPS:(04:50) What is Learnprompting.org(06:30) Learnprompting.org's adoption stats and the transition from open source to a business(10:21) Are we done with prompt engineering or is there more to be discovered?The key 2-3 things every user of GPT should know(19:11) The format trick(21:32) Role casting / persona prompting(24:44) Does the level of vocabulary you bring to a LM impact its performance (26:10) Contrastive chain of thought(28:30) Language models responding well to negative instructions(32:29) Benchmarking techniques(34:00) Answer engineering(37:29) Debugging a prompt (41:13) Sander's favourite models todayBest practices for image prompting - maybe cut out(48:50) Productivity improvement with language models(51:25) Tips for coding with prompts(56:06) The current state of prompt engineering and how it'll evolve (58:30) 2024 will be the year of agents and Sander's favourite agents(59:57) How will agents impact the future of prompt engineering (1:04:10) How to start agent engineering(Adversarial attacks (1:19:40) Model to model hijacking(1:25:24) Chinese character attack (1:28:33) Taxonomy of attacksThis show is produced by Turpentine: a network of podcasts, newsletters, and more, covering technology, business, and culture — all from the perspective of industry insiders and experts. We’re launching new shows every week, and we’re looking for industry-leading sponsors — if you think that might be you and your company, email us at erik@turpentine.co.

An interview with Sander Schulhoff, creator of learnprompting.org, the largest prompting resource online. Watch the video version on YouTube: https://youtu.be/BNrPs3ANz1E References: ►Listen on Spotify: ►Listen on Apple podcasts: ►Learn prompting: https://learnprompting.org/ ►Twitter: https://twitter.com/learnprompting, https://twitter.com/Whats_AI ►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/ ►Support me on Patreon: https://www.patreon.com/whatsai ►Support me through wearing Merch: https://whatsai.myshopify.com/ ►Join Our AI Discord: https://discord.gg/learnaitogether Chapters: 0:00 Hey! I hope you enjoy this interview. Follow to learn more about AI and prompting with amazing upcoming guests! 00:30 Who are you? What's your background? 01:40 How where you involved with reinforcement learning? 03:10 How was AI used within Minecraft? 06:04 What made you transition into prompting? 07:03 What is learnprompting.org? 08:57 How does prompting work? What are common types of prompting? Is it only in English? 10:48 What can you improve with prompting vs. just natural language and talking with another human being? 13:49 How do LLM work? Why are they able to understand and generate text? Why can’t it be able to understand us like other humans do? 15:51 Is the main thing that LLM’s lack emotion? Or is there another big difference? 17:27 Can you give an example of where you used prompting? 19:11 Can you give more details on what is Lang Chain and how it’s used? 20:17 How can one benefit from using prompting vs. Googling the answer? 24:38 Can you get paid by being a prompt engineer? 25:55 What do prompt engineers do? What are their tasks? 30:40 Who can become a prompt engineer? 32:48 Is there any way to measure that you are improving and going in the right direction for improving prompts for a specific task? 35:24 How would you recommend people to learn prompting? 37:57 Is prompt engineering a job for the future? 39:00 Do you think prompt engineering might get better and better as to in the end replace a human? 40:40 What are the differences in prompting between two separate models and a model over time? 42:24 Is there a way to be sure you’re not specializing on only one model? 43:54 Is it important for prompt engineers to better understand models? 45:49 Could you explain the difference between fine tuning and prompting? 49:29 Are there any misconceptions that you often need to clarify to those new to prompting? 51:46 Do you see any clear limitations of prompting? 55:12 What are the biggest challenges you see for prompting? 56:23 Was GPT-3 trained to be unbiased? 58:43 Did you solve the issue of ChatGPT not having only one word? 01:00:38 Why do you think it’s so difficult to output only one word? 01:01:37 Do you think the model doesn’t listen or it doesn't understand? 01:02:55 Can you think of any ethical problems that may arise because of this new technology? 01:04:09 What are you currently working on? What’s your next project?

Join Sander Schulhoff, a specialist in prompt engineering, Philip Resnik, a computational linguistics professor, and Dennis Peskoff from Princeton as they delve into the cutting-edge world of AI. They explore the security risks of prompt hacking and its implications for military use. Discussion highlights include the evolving role of generative AI across various fields, innovative techniques for improving AI self-criticism, and the pressing need for energy-efficient large language models. Their insights offer a fascinating glimpse into the future of AI research.