Most AI fails because humans don’t excel at giving it what it needs. We fixed that.

Every UX researcher has been there: You feed your AI tool some vague project brief, cross your fingers, and get back generic questions that sound like they were written by an early-career PM. The classic "garbage in, garbage out" problem that's plagued computing since the dawn of time.

But here's the thing: The garbage isn't the AI's fault. It's ours.

The real problem nobody wants to admit

Some AI research tools will ask you to dump everything into a text box and hope for magic. "Describe your research goals," they say. So you write something like "understand user satisfaction" and wonder why the AI generates questions that could apply to literally any product on earth. On the other side of the spectrum are tools that’ll require you to input not just questions, but also follow-ups, essentially creating a dynamic survey rather than an AI-led interview. 

AI is only as intelligent as the context you give it. And most of us are giving it the equivalent of directions written on a napkin, or so many directions that AI systems are too constrained to manifest their abilities. 

Enter the five-question Craft With AI framework

At Genway, we’re always trying to make AI smarter. But part of doing that is about making humans better at instructing AI.

Instead of one overwhelming prompt or a long and frustrating list of questions, we guide every user through exactly five strategic questions that force clarity and precision:

1. What's your project's objective? (Not just goals—the real business driver and research gap)

2. What topics will we be covering? (Specific themes, assumptions, and research questions)

3. What would you consider a successful project outcome? (Concrete deliverables and impact metrics)

4. Who will we be interviewing? (Detailed personas, not just demographics)

5. Do you have any general project guidelines? (Tone, constraints, and special considerations)

Why this changes everything

Here's what happens when you force this level of specificity:

Before: "I want to understand why customers churn." After: Our system knows you're targeting Netflix subscribers showing early disengagement signals, that you're looking for emotional drivers behind declining usage, and that your end goal is feeding insights into predictive retention models.

The AI doesn't just generate better questions—it generates the right questions for your exact situation.

The Netflix test

Take a real example from our platform. A user came in wanting to understand customer churn. Generic AI tools would spit out basic satisfaction questions.

Our five-question framework revealed they were specifically targeting Netflix customers showing early disengagement signals—people who hadn't canceled yet but were showing specific behavioral red flags. The AI immediately pivoted to questions about content relevance, feature awareness, and renewal hesitations.

The result? Interview questions that could actually help predict and prevent churn, not just measure it after the fact.

The five-minute rule

Our users spend five minutes answering our framework questions. In return, they get interview guides that would take a senior researcher hours to craft manually.

Five minutes of structured thinking to eliminate hours of generic outputs.

And, we even help our users by offering a custom Genway GPT to eliminate most of the effort.

So no more garbage

We're not just solving a technical problem. We're solving a human problem: the gap between what we think we've communicated and what we've actually communicated.

Every breakthrough in AI adoption will come from better human-AI interfaces, not just better AI. The future belongs to companies that understand this distinction.

The era of "garbage in, garbage out" is over. The age of precision input is here.

Genway is revolutionizing UX research by making AI work the way researchers actually think. Ready to see what happens when AI gets exactly what it needs? Let's talk. Email us @ natan@genway.ai