Accurate generation strategies for large-scale projects
For large-scale project development, the following methods are recommended to improve the quality of generation:
- context-sensitive technology::
- Upload project structure description file (package.json, etc.)
- Keep at least 3 associated files open in the editor
- Use the "Refer to existing code" option (Ctrl+Alt+R).
- Advanced Prompt Tips::
- Adopt the template: "As a [Role], in an [Architecture] environment, implement [Function], subject to [Version] compatibility"
- Add constraint: "Must not use [expired API] and must handle [exception type]"
- Provide sample inputs and outputs
- Model combination application::
- Architecture design using Opus Works (for complex logic)
- Module implementation using Sonnet Poetry (performance balancing)
- Unit test generation using Haiku (fast response)
System Configuration Recommendations:
- Max plan user adjustable large context window (up to 20,000 points)
- Locally deployed users can train the Domain Adapter.
- Periodic cleanup of invalid contexts (via "Reset Session")
This answer comes from the articleAI Code Editor: Code Generation and Optimization Tool for Unofficial Claude Code SuitesThe
May not be reproduced without permission:AI productivity tools " How to improve the code generation accuracy of AI Code Editor in large projects?
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