In 43 minutes, I took the startup Duofuko, think Duolingo, but only swear words. And went from zero synthetic buyers to 81% of a virtual U.S. test panel chanting “take our money.” Ask Rally’s crowd “Would anyone pay for this?” and they start thinking about whether someone else would buy. When you re-ask the question—‘Pick yes or no’—the virtual panel lights up with pricing hooks, onboarding riffs, and marketing angles.
Same trick, different context: a logo test on X pulled 165× more responses in just 25% of the time when the prompt was reframed for commitment over consensus.
Prompting a stadium of AIs isn’t polling—it’s hosting a debate. Ask like a panel chair and every agent competes to win; ask like a pollster and they’ll mumble in unison. The rest of this article shows you how to enforce the former every time.
Why You Need To Rethink Prompting
When you’re starting out, it's easy to fall into the trap of treating Rally like a bigger, louder GPT: firing off a poll-style question (“How many of you prefer blue logos?”) and watch the agents converge on a bland midpoint. The result feels authoritative—lots of voices in loose agreement—but it’s the AI version of groupthink: useful friction gets smoothed away, edge cases disappear, and you’re left designing for nobody in particular. Research on the false consensus effect shows humans do the same thing: we overestimate how widely our own opinions are shared, so we stop probing for the outliers that drive breakthroughs.
Ask Rally for a show of hands or scenario specific decision as if you were talking to one user at a time and it crushes—genuine insight in minutes not months. Whereas if you’re prompting it a’la GPT (“helpful assistant”) or asking what they think others think, when what you really care about is how THEY think, then you’re prompting wrong.
The antidote to better synthetic research output in part lies in a small tweak to your prompting. Ask as if you were talking with one—and only one user—and let Rally ask them all at once. If that clicked, you can stop reading and get back to prompting. But if you’d like a few more illustrative examples, keep reading.
Prompt Comparisons To Unlock Flaming Hot Takes
Logo Preference Simulation
“How many people like blue logos?”
Why it fizzles — Agents don’t tally votes. You’d have to export CSV and custom script it or ask another LLM to count. Instead of exposing why a logo wins. You get averages, no angles.
Better prompt
“Which logo do you prefer?”
-
Navy -
Royal Blue -
Cyan -
…
Willingness to Pay Simulation
Bad prompt
“Which option would people pay for?”
Why it fizzles — Agents speculate about other people’s wallets. And we provided little context about what was on offer (Just Harder. Better. Faster. Stronger).
Better prompt
You've just created an account for a new social network to connect with people with shared interest, share memes, and feel like you're piercing the fake news media bubble... and you have the following options.
Which do you choose?
-
a) Refuse to pay, and let the app sell a weekly dump of your data to advertisers. -
b) Pay $4.99/month for an ad-free, fully private version. -
c) Pay $7.99/month for the identical version plus a flashy ‘Verified’ badge. -
d) Uninstall the app
Feature Prioritization Simulation
Bad prompt
How many Chinese would choose the better data integrations for your health and fitness app over tailored fitness plans?
Why it fizzles — Produces stereotypical insights. Risk of unwanted bias. Perhaps useful if you were studying one nation's perspective and understanding of another, but either you want to ask Americans or the Chinese. If the latter, take time to generate a new audience.
Better prompt
You have a friend that works at Strava, your health and fitness logging app. They're asking you for input on what you want to see improved next. What you say matters, and will influence which features you get to use sooner.
How do you think about the value of the following features? And which would you choose we prioritize and why?
Feature 1:
Better data integrations, allows you to easily export your activities as raw JSON
Feature 2:
Tailored fitness plans for hiking and cycling where you set a goal and # of training a week and it does the rest
Want me to look at your prompts and suggest improvements? Ping me on X.
Results & Pay-off
Switching from GPT-style to audience simulation prompts does two things almost immediately. First, it cranks up signal density: commitment-based questions reliably surface diverse responses and more exclusive insights in a single run—enough raw material to outline a positioning doc before your coffee cools. Second, it chops decision latency to the bone. The Duofuko experiment nailed product-market phrasing in under fifty minutes; the logo test hit a defensible creative direction in less. Where consensus once blurred ideas into a polite grey wash, Rally’s simulator prompts return sharp, color-coded arguments you can tag and route straight to the roadmap, copy deck, or pricing model.
The Habit to Keep
The recipe is simple but non-negotiable: frame every question as if you were talking with one person in a safe space, and watch how the role-playing agents must pick a lane. Disagreement becomes data, conflict becomes clarity, and the “stadium” you’re paying for finally plays at full volume.
The teams that lean into this virtual audience simulator prompting will ship braver features, clearer messaging, and bolder prices—months faster than those still running old school playbooks. Ready to test your skills? Sign up for our paid plans today, spin up your first panel and prove it. And if you’d like to join a community of virtual audience designers for peer-to-peer knowledge sharing, join here. See you on the other side.