How to generate synthetic data using generative AI
FEB 2024
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Generating synthetic data using generative AI involves a series of steps, starting with defining your parameters.
These parameters should reflect the characteristics of your target audience. Once these parameters are set, you can use Large Language Models (LLMs) to generate synthetic data that mirrors your target audience.
However, the quality of your synthetic data largely depends on the quality of your instructions.
The next step involves verifying your inputs and then generating your synthetic data, before validating and verifying the quality of the synthetic data before using it for any analysis, predictions, or insights.
For a step-by-step guide on how to generate synthetic data using generative AI, read our comprehensive guide here.
Try MindPort for better insights, faster ⬇︎
MindPort empowers researchers, data teams, and agencies to effortlessly generate synthetic data with unmatched efficiency. Whether you want us to run an end-to-end research project, or just generate the data for your in-house team, we use our proprietary data-driven platform to scope, synthesise, collect, analyze and share data with your team.
Using GPT can only get you so far. Inside MindPort, our data team can organize your projects, scope your requirements, generate a synthetic sample and track individual synthetic participants. We're able to generate validated data in a fully bespoke workflow. This means your teams get the data and insights they need, in one centralized location.
Take your insights and analytics a step further with MindPort's synthetic data and insights:
Even before generating synthetic data, we can tailor over 180 unique categories of variable, totalling over 700 individual parameters per participant, based on your data requirements.
We can scale up from one to one thousand participants, enabling us to generate entirely unique three-dimensional personas that reflect the complexities, nuances, and fidelity of your real-world data.
We take this further by using 3rd party verification and our review framework to benchmark generated data against input data, carefully checking for biases, errors, and miscategorisations.
We're then able to package your synthesized data and insights within a team workspace, incorporating quotes for a visually compelling presentation.
Notably transforms your data analysis process, making synthetic data synthesis a seamless and insightful journey from start to finish.
Interested? Contact us and we'll send you a synthetic sample for free.
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