AI写Demo很快，为什么一改老项目就容易错？

使AI从毫无基础开始编写一个小型工具, 只需短短几分钟便能够目睹界面呈现。让其针对一个持续运行多年的项目去改动状态字段, 然而却极有可能编写出一串串在表面上貌似合乎常理、然而在业务范畴内却全然错误的代码。情形并非一直是模型的强度不足, 也并非一直是提示词的详尽程度欠缺。偶尔, 实际所欠缺的乃是: 此系统未曾清晰地告知任何一个人, 某一个词到底所指的是什么意思。我曾从事过高频交易系统相关工作, 也曾进行过技术管理方面的事务。对于判断一次修改是否正确, 相较于仅仅关注新增代码能否运行, 我更为在意的是它是否遵循了原本的业务约束。概念插画, 有着外形相似的那些模块, 然而这些模块, 可不一定就契合旧系统的连接约束, 这里面的插画, 是通过AI生成的, 并非真实项目进行截图所得到的。Demo里可以定义规则老项目里要先找到规则当去新建一个Demo之际, “完成”或许仅限存在着单独一种释义, 那即为, 去轻点一下按钮, 状态便会由未曾完成转而变为已然完成。然而, 老项目里头的某一个字段, 常常在列表、后台、报表、通知以及外部接口方面一同将其用来用去。它所具备的名字或许显得较为简单, 可是, 其背后所存在的约定并非是简单的那种状态。在2026年8月27日, 在《- 》里分享了一个实践方向, 这个方向是让业务术语以及上下文归属变得更加明确, 以此来减少Agent对于旧系统含义的猜测, 这是作者的工程经验, 而非那种“加一份文档就能提高多少正确率”的实验结论。这个方向具备值得予以探讨的性质。然而, 我更加倾向于选用一个规模极小的例子, 以此来阐明问题具体出在什么地方。“订单完成”到底是哪一种完成以下使用虚构订单和模拟数据不对应任何真实账户或业务系统。产品提出一项需求, 该需求为在列表当中显示“是否已完成配送”, 现有数据里正好存在三个字段, 这三个字段分别是付款状态、配送状态、退款状态。一种写法呈现出较易操作之感, 其表述为: 一旦付款状态呈现为paid, 便会显示“已完成”。另外一种书写方式为: 唯有配送状态处于特定状况时, 才会显示“已完成配送”。两种皆可返回布尔值的写法, 也全部都能够经由这般通过是否为确实是true或者false这种检验情况唯这并非指向同一要解问题的应答 , 但它们回答的不是同一个问题。付款结束状况达成, 表明付款相应环节抵达特定情景配送完毕情形呈现, 意味着履约步入别样阶段。物品成功送达之后提交退款申请, 同样不会致使“曾经配送执行完毕”该既定情节自行当隐退不见。对应的三种业务含义是一个“完成”, 付款、配送以及退款, 这三者必须分别进行建模。我撰写了6个固定使用的例子, 分别是, 已付款但尚未发货, 处于运输的过程当中, 已经完成送达, 付款出现失败的情况, 送达之后申请退款, 还有取消了并且没有履行约定。在本地运行时所呈现的结果为, 存在这样的情况, 将付款当作配送的写法有4个得以通过, 另一种明确检查配送状态的写法有6个通过。然而, 有两个出现了失败的状况, 这两个失败的情况恰恰就是“钱已经支付了, 可是货物却还尚未送达”这种情形。这两个经由数, 并不代表任何AI模型之正确率, 亦非一次模型对照测试。两种实现, 皆是为教学而人工构造的, 样本亦是特意设计的反例。它仅能证明: 类型正确, 却不足以证明业务含义正确。针对模拟用例展开检查, 其中付款猜测方面通过的情况是4/6, 配送规则方面通过的情况是6/6, 这并非是模型评测。把所有地方的词都统一也可能统一错可能会有人这么想, 那把所有状态都改成统一的一个, 这样不就简单了呀。非得留意这个动作。给钱、运送以及售后原本皆为各异的进程, 强行归入一个“完结”的范畴, 兴许会将有价值的差异消除掉。在领域驱动设计当中的一种情况 , 一般会被翻译作限界上下文 , 它着重于在一个清晰明确的边界范围之内 , 使得含义始终保持一致 , 并且还要阐述不同边界之间是怎样相互对应的情况 , 并非是强行要求整个公司仅仅去使用一个模型。对于这一点有着清晰的说明。还有相关来源。针对日常开发而言, 一开始没必要重构成复杂的架构, 首先要明确, 这个页面所询问的是配送情况, 而非付款事宜, 配送状态是由履约模块来负责的, 退款状态绝不能被前端擅自解读为“从未送达”。这几句话就是比“请遵循最佳实践”更有用的上下文。改代码之前先给AI一份能核对的任务我会把任务拆成四个部分。首先, 来讲术语, 要把像“完成”“有效”“可用”“余额”这般容易出现一词多义情况的概念写清楚, 还得说明当下任务所使用的是其中哪一种含义。其二, 归属, 指明哪一个模块对这条业务规则负责, 哪些模块仅仅是进行展示或者消费结果, 前端能够读取一个字段, 并不代表它拥有重新定义字段的权力。其三, 不变之量需列出此次修改不容被破坏的事实, 诸如, 未送达情形下不可呈现实行配送完成的状态而申请退款之时不应去改写已然发生的配送实情。并且这些条件得经由业务负责人加以确认, 绝不能够让AI自己去胡乱编造事实。其四, 验收实例展示, 给出正常的输入情况, 给出异常的输入状况及其容易混淆于常态的输入版本, 以及相应预期能得出的输出结果, 使得测试过程先将业务含义予以固定, 而后再去查验代码是否达成了此业务含义。随后, 要先让AI去定位现有的实现情况, 以及调用方和测试情况。之后要解释准备改动的地方在哪里, 还要说明改动的原因是什么。要是代码和文档相互之间存在冲突, 那就先报告冲突情况。并且, 不要去挑选一个看起来比较顺眼的版本然后继续进行扩散。当前代码以及维护者, 这些资料是必须能够追溯到的。旧文档所写的内容里面, 越是充满自信的那一部分, 转变为可重复执行规则的可能性, 也就是把原先错误转变如此, 越存在极大的概率。AI能加快实现不能替团队决定业务含义凭借AI的能力可以比较适宜地协助去搜索与之相关的代码, 将调用关系进行有效的整理, 把反例予以补充, 把已经得到确认的规则去予以实现。然而那个“在客户口中所提及的完成究竟是属于哪一种完成”的情况, 依旧是需要由人来加以确认的。同样, 不可以将全部失败都归结于缺少文档。模型具备的能力, 上下文存在的限制, 测试拥有的质量, 代码存在的耦合, 以及任务涵盖的范围, 这些都会对结果产生影响。补充业务方面的语义, 这是一项能够进行检查的改进, 并非是包治百病的万能处方。一个规模较小且清晰明确的需求, 也不一定就值得去引入完整的DDD框架。首先要把当下修改所涉及的术语说清楚, 还要把归属说明白, 并且把验收条件阐释清楚, 通常其比一口气对整个项目进行改造更容易去验证。降下来了AI把“把想法写成代码”的成本, 然而它没有替团队补上那些从未说清的业务决定, 标点符号。所以, 在老项目之中, 最值得先行补充的内容, 并非一定是更长的提示词, 而就恰恰是这样一句话, 即: 我们究竟要通过怎么的方式, 才能够确切知道, 此次进行的修改, 在业务层面上确实是修改正确了呢?此文含有能够运行的模拟业务方面的用例, 并未调用模型去开展A/B测试, 并不代表任何AI产品的正确比率, 同时也不透露私有项目的代码。资料同用例进行核对的日期是: 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