The video demonstrates how to build a market research app using Cursor, focusing on generating demographic personas and collecting their responses to customizable questions, with results visualized in real-time through charts. The creator emphasizes structured data handling with JSON, implements asynchronous querying and threading for efficiency, and concludes with the importance of documentation for the app’s functionality.
In the video, the creator demonstrates how to build a market research app using Cursor, focusing on generating demographic personas and collecting their responses to a specific question. The app allows users to customize questions, such as preferences between iOS and Android, and provides real-time updates on the results through a chart. The final results are saved in a JSON file, making it easy to analyze the data collected from various personas.
The development process begins with setting up two empty files, main.py and GPT_call.py. The creator utilizes the OpenAI GPT-4 model to generate responses by creating a function that handles messages and retrieves chat completions. The video showcases how to structure the code to enable asynchronous responses, allowing multiple personas to be queried simultaneously. The creator emphasizes the importance of using JSON format for structured data handling.
Next, the video dives into generating demographic personas based on income, city, and profession. The creator uses GPT to expand on these attributes and create a diverse set of personas. Each persona is then asked a question, and the responses are collected in a structured format. The creator also highlights the need for reasoning behind each answer, which adds depth to the data collected.
As the app evolves, the creator implements threading to handle multiple queries at once, aiming for efficiency in collecting responses. However, they encounter challenges with cumulative results and data saving. The creator adjusts the code to ensure that results are aggregated correctly and saved only after all responses are collected, rather than during each iteration. This process is crucial for obtaining accurate insights from the data.
Finally, the video concludes with the implementation of real-time plotting of results, allowing users to visualize responses as they come in. The creator tests the app with different questions and demonstrates how the results are displayed dynamically. They also discuss the importance of documentation, creating a README file to explain the code’s functionality. The video serves as a comprehensive guide for building a market research app, showcasing the integration of AI and data visualization techniques.