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I built an AI that answers questions based on user research data.
A guide to building an AI with a custom knowledge base using OpenAI API.
Modern products often have a large amount of user research data from different sources: user research interviews, intercom conversations, customer e-mails, surveys, customer reviews on various platforms, etc.
Making sense of all that data is a challenging task. A traditional way to do that is to maintain a neatly organized database with various corresponding tags.
But what if we can have our personal AI chatbot that can answer any question about our user research data?
By querying a large amount of historic user research data, the chatbot can provide insights and recommendations for a new project, product, or marketing campaign.
Well, now it’s possible with just a few lines of code. You can do that even without a technical background. In this article, I’m going to explain how to do that step-by-step.
This technique was first described by Dan Shipper.
OpenAI API
You are probably familiar with ChatGPT and hopefully are already using it in your working process. If not, I recommend reading my article about AI's impact on design first.
OpenAI also provides an API to send the requests. We need that to be able to send the relevant context to the model. And to keep the information private.
Before we start with API, you can try interacting with GPT-3 model through a user interface in GPT-3 Playground.
Privacy concerns
There are many privacy concerns when we deal with user data. By default, OpenAI will not use data submitted by customers via our API to train OpenAI models or improve OpenAI’s service offering. But of course, there might be much more security limitations. Check OpenAI documentation for more information and consult with your legal team.
