Product Impact Podcast | Secrets to unlocking the value of AI

25. Designing Context for AI Systems [Paz Perez]

Product Impact Podcast | Secrets to unlocking the value of AI

Show Notes

Agents are taking over our work and our lives. The problem with agents is that unless they have the right context, they won't be able to automate that task you hate or buy that ticket you wanted. Our guest today is the expert on designing context and one of the most fun guests we've had.

What you'll learn in this episode: ➜ How to design context, and why it is the most important lever you have over an AI product. ➜ The risks of connecting too much context, and how to decide what an agent should never be allowed to reach. ➜ Why more context improves an agent while the wrong context breaks it, and how to tell which one you are adding. ➜ How to organize context as an ecosystem assembled fresh for every request, rather than a window you fill. ➜ What to prune when a system starts drifting, and why maintenance is a design decision instead of a cleanup task. ➜ Why the role being hired to do this work pays a median north of a hundred and eighty-eight thousand dollars, and reads like a job description for a designer who understands context.

Our guest is Paz Perez. Paz built the Gen AI for UX Teams program at Google, where more than nine hundred designers went through her training, and she coined the term model designer for the job of shaping how a model behaves rather than how its interface looks. She is now co-founder of Nothing Fancy AI, and her course on designing context has sold out every time she has run it. Before Google she was at Autodesk working on chat, machine learning and natural language processing, which put her inside generative AI before it broke into the open. And before any of that she spent ten years as an architect building public schools in New York, where scale, transitions and natural light shape how a building feels without anyone noticing the decisions behind it. She describes the work she does now in almost exactly the same words. Today's conversation matters because agents do get significantly better when you give them context, and the measured gains are large. They also get measurably worse when you give them the wrong context, retrieving with total confidence from material that had nothing to do with the task. Being trained on the power of context is the difference. We also have some news to share, partly inspired by Paz's amazing course. Arpy and I are also launching a course on Maven called Improve Your AI Agentic Product. It collects the methods we have heard from more than seventy guests on this show alongside the work we do at PH1 with Spotify, Microsoft and a long list of large institutions. Listeners get 20% off, and the link and the discount code are in the show description. So if you have taken a course like Paz's and built an agent, ours picks up where that leaves off: adding KPIs, measuring the outcomes it actually drives, and improving the quality of what it puts out. And a reminder that this episode is brought to you by our consultancy PH1. We are the preferred research & strategy partner for teams building intelligent experiences. We partner with teams to improve the customer-facing value of their AI-powered products. This includes taking your evals and KPIs to the next level. Find us at ph1.ca. Thank you for listening to the Product Impact Podcast — if you have feedback, guest recommendations, or want to chat — contact us. Hosted by: Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/ Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/

Featured guest: Paz Perez — https://www.linkedin.com/in/pazperez/ Nothing Fancy AI — https://www.nothingfancy.ai/ Designing Context for AI Systems (course) — https://maven.com/paz-perez/context-architecture

Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.com

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