Disrupting generative AI products with just-in-time consumer insights
Summary
Key Insights
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Building AI products requires giving up control over user experience as outputs vary uniquely per user.
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Traditional usability testing is insufficient for AI products; longitudinal testing with repeated sessions is critical.
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Human-in-the-loop 'Wizard of Oz' testing effectively simulates AI to rapidly test assumptions before building.
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Model drift in LLMs can cause unpredictable or inappropriate responses, requiring mechanisms like turn capping.
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Users form evolving relationships with AI assistants, which need careful design and testing over time.
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Many product orthodoxies based on fixed experiences expire with generative AI and must be reinvented.
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Failure early and often is essential in AI product development due to its inherent unpredictability.
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Users may lack mental models for AI assistants, making onboarding and user education a bigger challenge.
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Asynchronous, multi-threaded conversations are natural for humans but difficult for bots to handle accurately.
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AI can automate synthesis tasks, enabling humans to focus on creativity, accountability, and authenticity.
Notable Quotes
"Building on AI and generative AI means giving up control."
"If the folks that design this tech don’t know how it works, it’s okay if you don’t know either."
"Alignment inevitably means changing minds."
"Everyone’s gonna be wrong. If you’re right all the time, you’re probably not taking big enough swings."
"With LLMs, the product itself is no longer a dependent variable; experiences diverge per user."
"We are studying experiences in ecosystems, not just products anymore."
"Failing early is critical because you can’t control the model."
"The promise of an LLM assistant requires users to build new mental models while using it."
"Turn capping helps reset the model to prevent drift but introduces new problems around memory."
"Creativity, accountability, and authenticity are going to be the new markers of humanity."
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