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Great products don’t start with solutions,
they start with user signals.
Signals to Solutions is a newsletter where I explore how UX research reveals signals that contribute to product solutions.
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Classification Literacy: The AI Skill Library School Taught Me For AI-Assisted Qual Analysis
What an AI "theme" or a "category" leaves out turns out to be exactly the kind of question discussed in library school. In my first semester at grad school as an Information Studies/Library Science student, a professor had our class debate what counts as a sandwich — is a panini one? A hot dog? We worked through competing definitions, including the idea that a sandwich is one thing contained within two others: a slice of ham between two pieces of bread, or, in one extreme ver
Ying Chen
9月9日讀畢需時 7 分鐘


Towards AI-Native UXR: Field Notes 1
A working list of what I’ve been reading and watching to understand LLMs and agents on before folding it into my UX research practice. Welcome to a new, recurring thread inside Signals to Solutions: “Towards AI-Native UXR: Field Notes.” Think of it less as a syllabus and more as a running record of what I’ve actually been reading and watching to become AI-native as a UX researcher. This first entry has two goals, both before any UXR application of AI-assisted coding tools: un
Ying Chen
8月30日讀畢需時 5 分鐘


Choosing User-Centered Metrics for AI Products: A Research Design Problem
A look at how UX research can help choose and interpret metrics intended to reflect users’ experiences of AI features, and why the meaning of a user-centered metric depends on the research design. In this piece, I want to take a step back and ask an important question in UX research for AI products: what makes a metric meaningfully user-centered for AI products, rather than simply a number collected from users? And what does the research design need to make that number interp
Ying Chen
8月23日讀畢需時 8 分鐘


What Should an AI Shopping Agent Do? Discovery Research Has to Answer That First When Designing AI Shopping Agents
A high-level synthesis of how Discovery Research can help define user's shopping intents using examples from Amazon, The Home Depot, and Sephora's LLM chatbots. *Originally published on Signals to Solutions substack here. Before a product team decides how to build an AI shopping agent, there’s a prior question worth starting with: what should the agent actually help someone do? That question sounds obvious, but it can be easy to skip past once you realize that your team can b
Ying Chen
8月20日讀畢需時 7 分鐘


Evals Across an AI Product Team: Three Perspectives and the Essential Role of UX Research
A high-level look at AI evaluation across product management, data science, and UX research, as well as the essential role UXR plays in defining what “better” means. Welcome to Signals to Solutions, a newsletter about understanding products through user behavior, product strategy, and UX research. For this first article (originally published here), I want to begin with a sharing my perspective from reading and synthesizing select industry and academic sources about a word tha
Ying Chen
8月3日讀畢需時 10 分鐘
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