Special attention is needed to fully utilize the value of the tool:
1. Demand description techniques
- adoptionfunction point splitting method: Decompose complex requirements into atomic operations such as "user login" and "data query".
- replenishmentOperational constraints: Clarify non-functional requirements such as concurrency, data security, etc.
2. Resource management strategy
- Free quota monitoring: By
tcb env listViewing Resource Usage - environmental isolation: Create different CloudBase instances for test/production environments
3. Version control specifications
- It is recommended to establish a version baseline via Git as soon as the code is generated by AI
- utilization
.cursor/templatesCatalog saves reusable AI-generated templates
4. Security border setting
Important business logic needs to be manually verified AI-generated database rights configuration, cloud function authentication and other security-related code.
This answer comes from the articleCloudBase AI ToolKit: a toolset to help developers rapidly build and deploy AI applications to the cloudThe
May not be reproduced without permission:AI productivity tools " What are the key points to keep in mind when using CloudBase AI ToolKit in real projects?
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