Synthetic user research

Synthetic user research is an innovative automation tool that utilizes artificial intelligence to simulate participant responses to survey questions, providing quick and insightful feedback for idea testing and survey validation.


Created by Jacky Koh, the Synthetic user research tool is a cutting-edge solution designed to streamline the process of gathering user feedback. It operates by generating AI-based responses to a set of predefined survey questions, which are crafted to explore aspects of customer experience and satisfaction. The tool is adept at simulating a specific target audience, such as customer experience leaders in enterprises, and tailors the AI responses to reflect the nuances and perspectives of this group. With the ability to input a context for the survey, users can ensure that the AI-generated responses are relevant and focused on the particular area of interest, such as the improvement of manual customer experience tasks through AI.

Use cases

Use cases for the Synthetic user research tool include validating new product concepts by gauging potential customer reactions, testing marketing messages to see how they resonate with a target audience, and exploring customer pain points and preferences without conducting time-consuming and costly real-world research. It can also be used in academic settings to simulate research studies or in UX design to iterate on user interface elements based on simulated user feedback.


The primary benefit of the Synthetic user research tool is its ability to rapidly produce user feedback without the need for real-world participants, saving time and resources. It offers a unique opportunity to test ideas and survey questions in a controlled environment, providing immediate insights that can inform decision-making and strategy. Additionally, the tool's AI-generated responses are designed to be plausible and insightful, ensuring that the feedback is not only quick but also of high quality and relevance.

How it works

The tool works by accepting input parameters that include an array of survey questions, a context string, and a description of the target audience. These inputs are then processed by an AI model, specifically openai-gpt3.5-16k, which generates a table of responses from 25 synthetic respondents. Each respondent's answers are crafted to be contextually relevant and reflective of the target audience's characteristics. The output is a well-organized table that presents the AI-generated feedback in a clear and interpretable format, allowing users to quickly glean insights from the data.

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