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Prompt engineering is crucial for effective use of language models, ensuring proper context, few-shot examples, and thought generation.
LearnPrompting.org provides a comprehensive guide to interact with generative AI, improve prompt efficiency, and obtain greater benefits from AI usage.
Sander Shuhoff's paper explores prompt hacking and vulnerabilities of language models. The research includes a prompt hacking competition, taxonomy of prompt-based attacks, and suggestions to mitigate these vulnerabilities.
Prompt engineering can be challenging with factors like code organization and chunking strategies. Despite some limitations, prompt engineering shows significant productivity improvements, especially in software development tasks.
The podcast emphasizes the significance of prompt engineering in ensuring reliable outputs from AI models. When prompts are well-designed and validated, users can have a level of trust in the system without needing to review every single input and output.
The podcast discusses the role of agents in AI automation, highlighting their potential benefits in providing real-time assistance while reducing the need for constant supervision. Agent engineering becomes crucial in building job-specific agents that can effectively interact with systems and perform complex reasoning.
The podcast explores the concept of prompt injection and its implications on language models. The discussion delves into the various techniques used to exploit vulnerabilities, such as obfuscation, context switching, and task deflection. It emphasizes the need for application developers to implement robust defenses beyond prompt-based strategies.
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/2311.16119
SPONSORS:
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X/SOCIALS:
@labenz (Nathan)
@SanderSchulhoff
@learnprompting
@CogRev_Podcast
TIMESTAMPS:
(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 today
Best 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 attacks
This 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.
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