Three-stage optimization method to improve the quality of AI applications
Based on the features of GitHub Spark, the following process is recommended to enhance the application utility value:
- Foundation generation phase: Immediately after entering the core functional description, the preview interface verifies: 1) the basic interaction flow 2) the data storage mechanism 3) the responsive layout adaptability.
- Functional enhancement phase: Enhance the application by adding AI commands (example prompts: add chatbot to answer usage questions/add automatic weekly report generation)
- Code fine-tuning phase: Utilizing the built-in code editor: 1) Adjusting CSS styling details 2) Supplementing input validation logic 3) Integrating 3rd party APIs (via Spark SDK)
Key Tip: Multi-device testing for key features (especially mobile PWA performance), complex features are recommended to be broken down into sub-features to be generated separately before combining. Refer to GitHub Actions for automated test configurations that can further improve stability.
This answer comes from the articleGitHub Spark: Rapidly Build and Deploy Mini-Apps with Natural LanguageThe
May not be reproduced without permission:AI productivity tools " How to improve the usability and functionality of AI-generated applications?
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