![]() If the API of your application can accept an array of products for creation, you can add the n parameter, which allows you to configure the number of responses. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. The temperature parameter allows us to adjust the predictability of the model's responses. In the content field, we specify what we want to receive from the model in response, with the exact expected JSON structure defined on the Step 1. Let's construct the body of the request to the OpenAI API. To generate the items, we need to send a request to the following endpoint:, which allows us to obtain a response for the given chat conversation. In Pre-request script let's define JSON structure (to be able to easily change it before converting to string) and store it as a collection variable. Let's create a collection called "Create Test Data" and the first request called "Generate Test Data". ![]() This is where the OpenAI API comes to our aid. To do this, we will use the public API which provides the ability to write our data via POST request in the following format:Įnter fullscreen mode Exit fullscreen modeĪgreed, manually creating a dozen different and realistic items for subsequent testing can be challenging. ![]() And in our test environment, the products themselves don't exist yet, so we need to create them. Let's assume we have a web application with a catalog of the most popular laptops. So, how to generate realistic and diverse test data without high time costs? To obtain such result AI generation can be used. Manual creation of test data is an option, but if the schema of one data object is complex and a large number of objects are needed, it becomes time consuming and impractical. The realism of test data is especially important for a product demo. We can use random data generators, but in this case, we lose the relevance to the subject area of the tested product. It is quite difficult to navigate through such a set. If we have an environment designed specifically for testing, the data often consists of identical values with the word "test". This article demonstrates how to generate test data using the OpenAI API in Postman and automatically send it to your server. However, with the advent of AI, it has become much easier. How to generate diverse test data? And what if you need a realistic dataset for a product demonstration? Test data generation can be a challenging task.
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