Summary
LLMs are everywhere, but when it comes to real research, they often fall short. Generic LLMs weren’t built for continuous research workflows, and product researchers quickly see the problem: the outputs are generic, lack full context, and struggle to connect multiple data sources. Instead of surfacing meaningful insights, they can amplify noise. In this session, Daniel will break down why AI often fails research teams and what’s missing. He’ll show how to make AI actually useful for continuous product research. Accelerating analysis, connecting insights across sources, and keeping researchers at the center, equipped with a powerful tool rather than replaced by one.
Key Insights
-
•
AI in research struggles with large datasets, often averaging results and missing subtle but important signals.
-
•
Curating and filtering datasets by removing irrelevant data improves AI research output quality.
-
•
Scoping research into focused projects or topics helps AI deliver more precise responses.
-
•
Asking one question at a time significantly enhances the quality of AI-generated answers.
-
•
Providing detailed contextual information (personas, company background, product details) to AI boosts specificity and nuance in responses.
-
•
AI hallucinations and trust issues necessitate human-in-the-loop processes to verify output quality and citations.
-
•
Iterative refinement of AI outputs, similar to app development, is critical for achieving polished research results.
-
•
Spot checking AI-generated citations can be an effective and efficient way to validate research quality.
-
•
Context passed as embedded knowledge rather than repeated in prompts yields better AI results.
-
•
Using multiple specialized AI agents to critique each other’s outputs can mitigate bias and improve research accuracy.
Notable Quotes
"AI has this strange weakness that when working with a large dataset, they often miss crucial, subtle findings."
"The larger the dataset you work with, the more costly it is to run a single operation on AI models."
"Whenever possible, you should be breaking down your work into specific research projects or topics."
"When you ask a question, try to ask one at a time so the model doesn't get lost."
"Context is everything — providing AI with a folder of your company’s knowledge makes responses more detailed and useful."
"Research with AI requires as much iteration and verification as building an app or prototype."
"AI-generated research reports should always be tied to real feedback that you can verify behind every sentence."
"There's no way to deny it: every industry needs to adapt to AI, but nobody really knows how yet."
"Human in the loop means constantly interacting with AI, documenting your thoughts and assuring quality."
"Some engineers build a council of agents that debate and generate responses, which can help with bias and accuracy."
Or choose a question:
More Videos
"We need honest, transparent conversations about futures because the impacts can be huge and wide-ranging."
April ReaganLook, Think, Act: The Futures-Smart Design Organization
October 1, 2021
"Resilient design needs to bend without breaking — to survive stresses while still delivering value."
Craig VillamorResilient Enterprise Design
June 8, 2017
"Systems thinking is an essential part of my own portfolio or playbook as a design leader."
Uday GajendarTheme One Intro
June 6, 2023
"Root cause analysis helps identify not just what is happening, but why it's happening, especially when more people have access to data."
Jen Cardello Dr. Shadi Janansefat Alex WrightCurating insight: Strategies for integrating knowledge across research functions
March 11, 2025
"The web content accessibility guidelines emphasize thinking about different ways people interact with our content."
Phil HeskethDesigning Accessible Research Workflows
September 29, 2021
"If you are a cohort participant and don’t see your private Slack channel, please let us know so we can guide you."
Bria AlexanderOpening Remarks
November 17, 2022
"Getting everyone to agree on what research should answer and in what order is where the fruitful back and forth happens."
Sarah Alvarado Nalini P. Kotamraju Anne Mamaghani Peter MerholzHow to make UX research leadership more effective [Advancing Research Community Workshop Series]
October 26, 2023
"Movement building requires healing, grief work, and physical movement to sustain people."
Deanna ZandtThe Unspoken Complexity of “Self-Care” with Deanna Zandt
July 21, 2022
"Doing nothing is not a neutral act. Ignorance may be bliss, but it doesn’t absolve you from responsibility."
Craig VillamorDesign Systems for Ethical Design
January 26, 2023
