Community Case Study: Structured constraints improve code generation stability by 10x

[linyy750911]

Hi 360 Zhinao team,

Summary

A community developer (non-professional programmer) ran a controlled A/B test showing that structured output constraints ("Code Bagu" / 代码八股) can improve LLM code generation stability by an order of magnitude — with domestic Chinese models performing exceptionally well under constraint.

Key Metrics

Scenario	Violations	Latency	Rounds
Unconstrained general LLM	34 structural violations	8000-word thought chain, then dead loop	N/A
With structured constraints	0 violations	3.2s	2 dialogue rounds

Test Details

• Task: HTTP health checker (multi-threaded + caching + exception handling)
• Modules tested: 3 Python modules of increasing complexity
  • Simple: Config hot-reloader (passed with 1 retry)
  • Medium: HTTP health checker (passed first try)
  • Complex: Student data reporter (passed first try)
• Constraint system: Code Bagu — enforces fixed structural format (八股 paragraph structure, symbolic duality checks, format consistency)

Core Insight

Not a model competition, but an engineering methodology competition.

Domestic models (tested with DeepSeek v4-pro) under structured constraints achieved stable, fast, compliant code generation that outperformed unconstrained general LLMs.

Links

• Full article (Chinese): https://zhuanlan.zhihu.com/p/2038534207488648441
• Open source tool: https://github.com/linyy750911/bagu-coding
• Compliance test report: Code with constraints passes all validation; unconstrained code fails with 34 violations

Suggestion for 360 Zhinao

If 360 Zhinao considers adding structured output constraint options to the API/SDK layer, it could become a significant differentiator for enterprise developers seeking deterministic, auditable code generation.

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Submitted as a community case study. No collaboration or resources requested — just sharing data that may be useful for your developer relations or technical evangelism.