Log in or create a free Rosenverse account to watch this video.
Log in Create free account100s of community videos are available to free members. Conference talks are generally available to Gold members.
Building impactful AI products for design and product leaders, Part 2: Evals are your moat
This video is featured in the AI and UX playlist.
Summary
The secret ingredient for impactful AI products is “evals”—an architecture for ongoing evaluation of quality. Without evals, you don’t know if your output is good. You don’t know when you’re done. Because outputs are non-deterministic, it’s very hard to figure out if you are creating real value for your users, and when something goes wrong, it’s really tricky to figure out why. Simply Put’s Peter van Dijck will demystify evals, and share a simple framework for planning for and building useful evals, from qualitative user research to automated evals using LLMs as a judge.
Key Insights
-
•
AI product development involves three layers: model capabilities, context management, and user experience, with evals central to experience quality assurance.
-
•
Automated evals help scale testing of AI with inherently open-ended inputs and outputs, enabling faster iteration cycles with confidence.
-
•
LLMs can serve as judges (evaluators) of other LLM outputs, which works because classification is cognitively easier than generation.
-
•
Defining what 'good' means for an AI system is a detailed, evolving process informed by research, domain expertise, and observed risks.
-
•
A three-option evaluation (e.g., yes/no/maybe) works better than fine-grained scales for consistent automated scoring by LLMs.
-
•
Synthetic data, generated by LLMs based on manually created examples, efficiently expands dataset breadth and usefulness.
-
•
Domain experts are essential for tagging data and establishing quality criteria, especially for high-stakes areas like healthcare or legal.
-
•
Building effective evals requires substantial effort—expect 20-40% of project resources devoted to this work.
-
•
Cultural differences impact subjective evals like politeness, requiring localization and careful domain definition.
-
•
AI product quality management is a strategic ongoing commitment, extending beyond initial development into production monitoring and iteration.
Notable Quotes
"AI products almost always have both open-ended inputs and outputs, which makes testing really hard."
"You have to build a detailed definition of what is good for my system to do meaningful automated evals."
"It’s much easier to classify an answer than to generate an answer, and that’s why LLM as a judge works."
"You don’t want to give too many options like rating from one to ten because consistency gets lost between different LLM calls."
"Synthetic data is useful because it’s easier to generate more examples of something you already have than to create entirely new data."
"If you launch in the US and politeness is an issue, first try to fix it with prompts; only if that fails should you build an eval."
"Evals are really your intellectual property—they define what good looks like in your domain."
"Domain experts are crucial for tagging data because users might say ‘that’s great,’ but experts can tell it’s totally wrong."
"You should plan 20 to 40 percent of your project budget on evals—it’s a lot more work than most people expect."
"This is where UX and product strategy bring huge value—defining what good means rather than leaving it to engineers alone."
Or choose a question:
More Videos
"Craft is not just about a beautiful final object, but a facilitative anchor that enables productive teamwork across departments."
Uday GajendarThe Wicked Craft of Enterprise UX
May 13, 2015
"Uganda is Africa’s largest refugee host nation with over 1.5 million refugees, 60% of whom are children—yet this crisis is often invisible in media."
Liz EbengoThe Burden on Children: The Cost of Insufficient Post-Conflict Services and Pathways Forward
December 4, 2024
"Flexibility is the network's ability to reconfigure itself and yet retain its goals; scalability means expanding or shrinking size with little disruption; survivability means withstanding attacks to nodes or codes."
Gordon Ross12 Months of COVID-19 Design and Digital Response with the British Columbia Government
December 8, 2021
"Design ethics can impact some harms of new technology but not all."
Alexandra SchmidtWhy Ethics Can't Save Tech
November 18, 2022
"Links are first-class citizens in hypertext note-taking; connecting notes brings ideas to life and reveals new relationships."
Jorge ArangoExploding the Notebook: How to Unlock the Power of Linked Notes (2nd of 3 seminars)
April 19, 2024
"AI is not just a tool for productivity or efficiency; it's a design material with unusual strengths and weaknesses."
Josh Clark Veronika KindredSentient Scenes and Radically Adaptive Experiences
June 11, 2025
"Accepting the need to change is not a normal instinct because it runs counter to survival."
John Maeda Alison RandAbout Design Organizations
May 13, 2019
"We do experiments not to make things work but to learn something new."
Dan WardFailure Friday #1 with Dan Ward
February 7, 2025
"Naming things like the 'vibe shift' is powerful in shaping the narratives around future developments."
Devon PowersImagining Better Futures
March 9, 2022
