上下文工程即代码——可靠人工智能开发的系统方法
1 分•作者: cogeet_io•7 个月前
我一直对 AI 编程助手的表现不稳定感到沮丧，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 代理失败并非模型失败，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 5 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 种以上的先进测试策略
- 文档即代码 - 自动化、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）
- 它为 AI 参与者系统地组装全面的上下文，类似于基础设施即代码系统化了部署。
早期结果：AI 任务成功率提高 10 倍，调试时间减少 50%。
所有规范都是开源的，并附带可立即使用的模板。
GitHub：https://github.com/cogeet-io/ai-development-specifications
期待社区的反馈 - 您在使用 AI 编程一致性方面有什么经验？
或者您可以在 X 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