上下文工程作为代码 – 可靠的人工智能开发的系统化方法
我对不一致的AI编码助手结果感到沮丧，因此我研究了这个问题并构建了一个系统化的解决方案。
核心见解：大多数AI代理的失败并不是模型失败，而是上下文失败。AI获取的信息不完整或结构不良。
我创建了五个规范，将AI开发从试错转变为系统工程：
- 规范即代码 - 系统化的需求定义
- 上下文工程即代码 - 解决“上下文失败”问题
- 测试即代码 - 15种以上的高级测试策略
- 文档即代码 - 自动化的动态文档
- 编码最佳实践即代码 - 可执行的质量标准
上下文工程规范是关键创新（特别感谢Tobi Lutke和Andrej Karpathy）——它系统地为AI参与者构建全面的上下文，类似于基础设施即代码系统化部署的方式。
早期结果：AI任务成功率提高了10倍，调试时间减少了50%。
所有规范都是开源的，并提供可以立即使用的模板。
GitHub: https://github.com/cogeet-io/ai-development-specifications
我希望得到社区的反馈——你们在AI编码一致性方面的经验如何？
或者你可以在X平台上联系我: https://x.com/Cogeet_io
查看原文
I've been frustrated with inconsistent AI coding assistant results, so I researched the problem and built a systematic solution.<p>The core insight: Most AI agent failures aren't model failures, they're context failures. The AI gets incomplete or poorly structured information.<p>I created 5 specifications that transform AI development from trial-and-error into systematic engineering:<p>- Specification as Code - Systematic requirement definitions
- Context Engineering as Code - Solves the "context failure" problem
- Testing as Code - 15+ advanced testing strategies
- Documentation as Code - Automated, living documentation
- Coding Best Practices as Code - Enforceable quality standards<p>The Context Engineering spec is the key innovation (big ups to Tobi Lutke and Andrej Karpathy)
- it systematically assembles comprehensive context for AI actors, similar to how Infrastructure as Code systematized deployment.<p>Early results: 10x improvement in AI task success rates, 50% reduction in debugging time.<p>All specifications are open source with templates you can use immediately.<p>GitHub: https://github.com/cogeet-io/ai-development-specifications<p>Looking for feedback from the community - what's been your experience with AI coding consistency?<p>Or you can hit me up on X: https://x.com/Cogeet_io