đ Hey, Iâm Nikki. Each week I write about UX research strategy, communicating impact, and using AI to do your best work. For more: Claude Skills Bundle | AI Prompt Library | Team Training
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Most researchers I know have the same story. The same raised eyebrow. The same deep sigh before they open another LLM convo and brace for disappointment.
The first time I tried to use AI for something âsimple,â I remember asking it to help me dissect a stakeholder brief. I pasted the vague, chaotic message, something along the lines of âWe need to test the new dashboard design before Friday; can you put something together?â and waited for help.
What I got back looked like someone had skimmed a UX blog from 2012 and stitched together a few polite sentences. It read like a student trying to impress their professor without actually doing the assignment. No context. No understanding of the politics behind the request. No reading between the lines. Definitely no sense of the real decision hiding underneath the pretty words.
The problem is that weâve been throwing AI at UXR tasks with the same energy we bring to reheating leftover lunch. Fast. Distracted. Half-formed prompts that barely capture what we actually need. Then we blame the AI when it hands back something flat, vague, or straight-up wrong.
Most UXRs are stuck in this loop:
You give AI a tiny prompt.
It gives you a tiny answer.
You rewrite everything yourself.
You decide AI isnât ready.
Then you go back to doing everything the slow way.
But, at the same time, you really have been burned.
Youâve tried using AI to:
Clean up messy notes
Summarize a long research plan
Rephrase an insight for an exec
Draft a kickoff email
Clarify a brief written by a PM who sprinted through it between meetings
And the output felt like it came from someone who wasnât in the room with you.
Iâve spoken to senior UXRs in fintech, SaaS, marketplaces, health tech, people who run teams, shape roadmaps, and handle cross-functional chaos every day, and every single one of them says something like:
âI can see the potentialâŠbut I donât trust it.â
Not because AI is bad.
But because nobody taught UXRs how to use AI in a way that respects the complexity of our work. We didnât get training.
Weâre self-teaching in the middle of deadlines. Weâre experimenting with prompts in between interviews. Weâre trying to make sense of output that feels helpful one minute and deeply misguided the next.
AI becomes incredibly powerful for researchers once you give it the kind of direction your craft already relies on which is precision, context, constraints, intention, and the decision youâre supporting.
The magic doesnât come from the model. The magic comes from your brain, paired with a structure that helps the AI act like a competent partner instead of an overeager intern.
Most researchers give up after a few half-hearted prompts. You ask something generic. It spits out something shallow. You move on. Itâs not that AI canât help you think better, it can, but only if you know how to push it.
Now weâre going to walk through the entire research process, from messy stakeholder kickoff to crisp, confident insights, and turn AI into the kind of co-pilot youâve wished for since your first week as a researcher.
Why Pancake Prompts Fall Flat
If youâve ever asked AI for help and felt mildly offended by the output, youâre not alone. Most researchers start with tiny prompts, get tiny answers, and assume the model just isnât good enough. Itâs the same energy as handing someone a sticky note that says write the whole report for Monday? and expecting them to read your mind, decode your org politics, and magically land on something useful.
The problem isnât the AI. The problem is the prompt.
I know that sounds like the kind of patronizing advice thrown around LinkedIn, but stay with me. I spent months testing how UXRs actually prompt AI across dozens of real projects, interviews, surveys, strategy sessions, prototype tests, you name it, and most of the prompts UXRs write fall into the same patterns:
1. The âdo everything for meâ prompt
Example: âWrite a usability test.â
What the AI hears: âGuess wildly.â
2. The âhereâs a crumb, bake a cakeâ prompt
Example: âHelp me write a kickoff doc.â
What the AI hears: âPlease hallucinate intentions for me.â
3. The âIâll tell you the task but not the stakesâ prompt
Example: âSuggest tasks for a survey.â
What the AI hears: âThrow generic content at the wall.â
When you feed AI a prompt that thin, you get output that reads like the UX equivalent of a cookbook written by someone who has never eaten food. Lacking any awareness of real-world messiness.
AI has no idea what you actually care about unless you tell it.
And UX research is built on a whole lot of context:
Why the team wants this research
What decision sits behind the request
Whoâs pushing for speed
Whatâs riding on the outcome
What happened last time someone skipped research
Who will use the insights
What the constraints look like
Which trade-offs matter
What business metric is at stake
How much is already known
Whatâs being assumed without evidence
When your prompt doesnât include these pieces, youâre asking an AI model to work blindfolded. I started experimenting with a completely different approach: stop treating AI like a vending machine, and start treating it like a very fast, very literal junior researcher who needs a real brief.
This is where the FAST model came from. A simple four-part structure that upgrades almost any prompt instantly.
Below, I walk you through the exact system that turns AI from âovereager internâ into a reliable research co-pilot:
The FAST model (the 4-part prompt structure that fixes pancake prompts instantly)
Before/after examples that show what âgoodâ looks like
Copy-paste prompts for kickoff, decision-mapping, risk surfacing, and assumption-breaking
Mid-study checkpoint prompts to stop projects drifting off a cliff
Synthesis guardrails so you get support without handing over judgment or raw data
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