Practical ways to improve the quality of kimi-cc code generation
The quality of code generated by Kimi's model can be significantly improved by the following strategies:
- Precise cue word design::
- Include a specific technology stack (e.g., "Implement XX functionality using Python 3.10's match syntax")
- Clarify input and output requirements (e.g., "function takes a Pandas DataFrame parameter and returns a sorted copy")
- Add constraints (e.g., "time complexity does not exceed O(nlogn)")
- Parameter Tuning Tips::
- Control response length with -max-tokens (1000+ recommended for complex tasks)
- Adjust creativity with the -temperature parameter (most stable in the 0.3-0.7 range)
- Enable the -stop parameter to define termination conditions (e.g., stop when a specific comment is encountered)
- Iterative Optimization Process::
- First round of generating the base code framework
- Secondary prompts based on error messages (include error reporting information in new prompts)
- Explaining complex code segments using the kimi-cc explain function
Advanced Tips:
- Preset style guides (code specification requirements are stated in the prompts)
- Context-aware generation in conjunction with git history (self integration required)
- Manual screening after generating multiple scenarios in parallel using multiple tokens
This answer comes from the articleKimi CC: An Open Source Tool for Driving Claude Code with Kimi ModelsThe
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