Optimizing a Prompt for Production
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Trial and error can only get you so far when working with generative AI, because when you're running a prompt hundreds or thousands of times a day, you need to know when and why it fails. Prompt engineering isn't about finding the right combination of magic words that tricks the AI to do what you want, it's a process for building a production-grade AI system that delivers the results you need, reliably and at scale. We'll apply prompt engineering principles to a real-world AI use-case and make the strategic trade-offs needed to make your AI products economically viable. If you have tried prompting to automate a task, but couldn't get good enough results, this talk will give you actionable steps for closing that gap. You'll take away a checklist for optimizing prompts from idea to production, using principles that are transferable across models and modalities.
Transcript
Trial and error can only get you so far when working with generative AI, because when you're running a prompt hundreds or thousands of times a day, you need to know when and why it fails. Prompt engineering isn't about finding the right combination of magic words that tricks the AI to do what you want, it's a process for building a production-grade AI system that delivers the results you need, reliably and at scale. We'll apply prompt engineering principles to a real-world AI use-case and make the strategic trade-offs needed to make your AI products economically viable. If you have tried prompting to automate a task, but couldn't get good enough results, this talk will give you actionable steps for closing that gap. You'll take away a checklist for optimizing prompts from idea to production, using principles that are transferable across models and modalities.
About the speakers
Founder & Co-Author of "Prompt Engineering for Generative AI"