A guide to avoiding the pitfalls of code generation
A comprehensive program to improve code quality:
- context constraint: Clear designation
"生成Python3.9代码，需包含类型标注和异常处理"and other requirements - Example Driver: Provide input and output samples (3-5 sets) using Few-shot prompting.
- static check: Integrate pylint/ESLint to validate generated code on-the-fly, setup
max_retry=3autocorrect
Professional-grade optimization:
- Mix of The Stack and HumanEval datasets for fine-tuning (3:1 code-to-interpretation ratio)
- For specific frameworks (e.g. PyTorch), load the corresponding version of the API documentation as a knowledge source
- start using
chain_of_thought=TrueLet the model be annotated before coding
Typical problem management: when a hallucinatory API is present, theprompt="请检查以下代码中不存在的库函数：[代码片段]"Triggers self-correcting mechanism, suggests eventual verification of functional correctness via unittest
This answer comes from the articleQwen3-8B-BitNet: an open source language model for efficient compressionThe
May not be reproduced without permission:AI productivity tools " How to avoid common defects of Qwen3-8B-BitNet in code generation?
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