How to use Chat GPT to Generate Contextual Test Data
Are you looking to generate contextual test data using Chat GPT?
If you are looking for a way to generate contextual test data quickly and easily, look no further than Chat GPT, the new artificial intelligence (AI) system for creating text from context. Rather than manually typing out text, Chat GPT enables developers and testers to quickly generate natural sounding test data with just a few clicks. Using Chat GPT to generate test data is incredibly simple.
All you need to do is provide the system with some data about the context in which you need to generate test data. This can include a few key words associated with the situation, such as customer service inquiries or online purchases. Then, Chat GPT will produce text by using its AI capabilities to contextualize the information you provide. In addition to producing highly contextual text, Chat GPT is also able to refer back to previously generated statements to help it generate more congruent sequences of dialogue.
This means that you can contextualize the test data that Chat GPT produces, as it's been specifically designed to mimic real-world datapoints. Using the generated test data from Chat GPT, developers and testers can quickly and easily assess the performance of their applications and websites in a variety of different contexts. This method is not recommended to be used for any real analysis, nor should you base any decisions based on the data that is generated from Chat GPT, rather it provides a way to create applications and data developers without needing to get the real data.
Now, let us break down the steps to follow using Chat GPT:
1.Register a free OpenAI account. GPT-4 (openai.com)
2. Go to Chat GPT (https://chat.openai.com/chat ) and start writing some instructions. Once finished, hit ENTER.
3. Analyze the results.
4. Refine your instructions if necessary.
5. Ask chat GPT to write a python script that contains the instructions from Step 4.
6. Copy python´s generated script.
7. Paste the generated script on a text file and save it with the .py extension.
8. Run the .py file (in this example I´m running the file on Windows by double clicking the file using the Python Launcher. Python should be installed on your machine).
9. A .csv file should be generated in the same folder that the .py file is located.
10. Open the .csv file to check if it was populated with records.
11. Upload the .csv file on a Tableau Workbook.
12. Use this data to feed your Tableau Dashboard.
It is important to note that the limitations of the data are based on the inputs that are provided. By providing inputs for the desired columns of test data, you are able to contexutalize rows for testing purposes and design whatever scenario is needed for testing. This method can be applied to other LLM models as well and it can have great applications for the design and development of analytics and applications. You can trust the data is relevant to your desired scenario.