Can I simulate wisdom of the crowd feedback from virtual customers?
ChatGPT is designed to be a helpful assistant, but can it help you with customer research? By getting AI to roleplay as your potential customers, you can get more diverse and realistic feedback without paying for a focus group or survey.
Personas of Thought
A new technique for synthetic market research with virtual audience simulations, giving your diverse and unique answers...More
I keep hitting a wall when trying to predict how different parents will react to our new smart bib features. One perspective just isn’t enough—there are so many types of users out there.
If only I could gather insights from a whole crowd of virtual customers, each with their own unique viewpoint. That kind of feedback would save us months of guesswork.
But how do I simulate that wisdom of the crowd without actually assembling a panel of hundreds of testers?
I need a method that lets me generate diverse opinions and then combine them into a clear direction—something smarter than just asking one expert.
Maybe this “Personas of Thought” technique is exactly what we need to finally crack this problem.
This course is a work of fiction. Unless otherwise indicated, all the names, characters, businesses, data, places, events and incidents in this course are either the product of the author's imagination or used in a fictitious manner. Any resemblance to actual persons, living or dead, or actual events is purely coincidental.
**Personas of Thought: Harnessing Diverse Perspectives for Smarter AI Responses**
In the evolving landscape of AI and natural language processing, one of the challenges is getting responses that go beyond generic, surface-level answers. Typically, when we prompt a large language model (LLM) naively, the result tends to be bland and predictable—often sycophantic, overly positive, and lacking in depth. To overcome this, a promising technique called *Percentages of Thought* has emerged, an adaptation of the popular chain-of-thought prompting, that leverages multiple personas to enrich AI responses with diversity and nuance.
At its core, Percentages of Thought invites the model to imagine a variety of different perspectives—either world-class experts or everyday personas—each contributing their unique viewpoint on a given question or task. Instead of a single, monolithic answer, the AI generates multiple diverse opinions, which are then aggregated into a thoughtful, well-rounded final response. This method has shown to produce far more insightful, non-obvious, and human-like answers.
### Experts Prompt: Wisdom from the Top
One version of this technique is the *Experts Prompt*. Here, the AI is asked to first name several world-class experts relevant to the question—past or present—and then answer from each of their perspectives. For example, when considering the best way to learn a new skill, the prompt might include Malcolm Gladwell, Angela Duckworth, Tim Ferriss, and Richard Feynman. Each expert offers a distinct viewpoint: Gladwell emphasizes 10,000 hours of practice, Duckworth focuses on grit, and Ferriss shares practical hacks.
By aggregating these expert opinions, the final response becomes richer and more comprehensive than one generated naively. The experts prompt encourages critical thinking and diversity of thought, exposing insights that would otherwise be overlooked. Additionally, it fosters a balanced critique—highlighting strengths and potential weaknesses—rather than simply endorsing an idea.
### Personas Prompt: The Power of the Crowd
Complementing the experts prompt is the *Personas Prompt*, which asks the AI to consider viewpoints from everyday demographic personas relevant to the task. For instance, when evaluating a product name, the AI might draw on the perspectives of a busy parent, an athlete, a fashion-conscious individual, and an industry expert. Each persona highlights different priorities—convenience, style, clarity of purpose—which collectively surface a more nuanced understanding of the problem.
This approach taps into the "wisdom of the crowds
All right. I'm going to walk you through a technique that I call percentages of thought. It's just a play on the chain of thought idea where you would get an LLN to spend some tokens, to think about the task first, before doing it. And this adaption that I I've been using more recently is that you ask it to come up with a bunch of different personalities or personas to demographic percentages or experts. Who will have a different opinion on on the task and then you can aggregate their opinions together to get the fund a result.
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