Why Your AI Resume Sounds Generic (And How to Fix It)
Why Your AI Resume Sounds Generic (And How to Fix It)
为什么你的 AI 简历听起来千篇一律（以及如何修复它）
I’m Larbi, and I build Roleframe, an AI tool that tailors resumes to specific jobs. I spend a lot of time looking at what large language models (LLMs) produce when you ask them to “improve” a resume, and the output is almost always the same: results-driven professional, leveraged cross-functional teams, orchestrated end-to-end solutions. 我是 Larbi，我开发了 Roleframe，这是一款为特定职位定制简历的 AI 工具。我花了很多时间观察大语言模型（LLM）在被要求“优化”简历时生成的内容，结果几乎总是千篇一律：“结果导向型专业人士”、“利用跨职能团队”、“编排端到端解决方案”。
If you’ve used ChatGPT on your own resume, you’ve seen it too. I wanted to write this for a developer audience because you already understand the machinery underneath the problem. This isn’t magic or a mystery. It’s next-token prediction, model routing, and prompt design. Once you see the resume through that lens, the fix becomes obvious and mechanical. 如果你曾用 ChatGPT 修改过自己的简历，你也一定见过这些词。我写这篇文章是面向开发者群体的，因为你们已经理解了问题背后的运作机制。这并非魔法或谜团，而是“下一个词预测”（next-token prediction）、模型路由和提示词设计。一旦你透过这个视角审视简历，修复方法就变得显而易见且机械化了。
What you’ll get here: why cheap models default to generic phrasing, the three tells recruiters catch, why a single prompt can’t tailor a resume properly, and a ten-minute audit you can run on any AI output before you send it. Let’s get into it. 本文将涵盖：为什么廉价模型倾向于使用通用措辞、招聘人员能看出的三个破绽、为什么单一提示词无法妥善定制简历，以及你在发送任何 AI 生成内容前可以进行的十分钟审计。让我们开始吧。
The unlimited AI trap: why your resume reads like a robot
“无限 AI”陷阱：为什么你的简历读起来像机器人
Most “unlimited AI” resume builders have a math problem they don’t advertise. If a tool promises endless rewrites for a flat monthly fee, it can’t afford to run the best, most expensive models on every request. So it routes your resume to the cheapest model that produces passable text. 大多数“无限 AI”简历生成器都有一个它们不会公开的数学难题。如果一个工具承诺以固定的月费提供无限次重写，它就无法负担得起在每次请求中都运行最顶尖、最昂贵的模型。因此，它会将你的简历路由到能生成“勉强合格”文本的最廉价模型上。
Cheap models play it safe. When they’re unsure what to say, they fall back on the highest-probability phrasing in their training data. That data is millions of existing resumes and job ads, so the model mirrors the average of all of them. The result is what recruiters call a resume monoculture: near-identical wording and structure no matter who the candidate is or what they actually did. 廉价模型倾向于求稳。当它们不确定该说什么时，就会退回到训练数据中概率最高的措辞上。这些数据包含数百万份现有的简历和招聘广告，因此模型反映的是所有这些数据的平均值。结果就是招聘人员所说的“简历单一化”：无论候选人是谁或实际做了什么，措辞和结构几乎完全相同。
You feel it as vagueness. Padded metrics, filler verbs, and summaries that describe a job title instead of a person. The tool isn’t broken. It’s doing exactly what a low-compute model does when nobody paid for anything better. 你会感觉到一种模糊感。充水的指标、填充式的动词，以及描述职位头衔而非个人的总结。工具本身没坏，它只是在没人付费购买更好服务的情况下，执行了低算力模型该做的事。
Cheap models vs. frontier models: the hidden downgrade
廉价模型与前沿模型：隐藏的降级
There’s a real quality gap between the cheap models behind “unlimited” plans and the frontier models that cost more to run. The difference isn’t grammar. Both write clean sentences. The difference is judgment: how well the model reads a job posting, matches it to your experience, and picks specific language over safe language. “无限”计划背后的廉价模型与运行成本更高昂的前沿模型之间存在真正的质量差距。区别不在于语法，两者都能写出通顺的句子。区别在于判断力：模型阅读职位描述、将其与你的经验匹配，以及选择具体语言而非安全语言的能力。
A stronger model notices you shipped a payments feature under a deadline and writes a bullet about the trade-off you made. A cheaper model writes “improved operational efficiency” and moves on. One reads like a person who was in the room. The other reads like a template. 更强大的模型会注意到你在截止日期前交付了支付功能，并写出关于你所做权衡的要点。而廉价模型只会写“提高了运营效率”然后草草了事。前者读起来像是一个亲历者，后者读起来则像是一个模板。
The “unlimited” pitch hides this downgrade. You’re told you can generate as many resumes as you want, and technically you can. What you’re not told is that every one ran through a model chosen for cost, not quality. “无限”的宣传掩盖了这种降级。你被告知可以随心所欲地生成简历，从技术上讲确实如此。但你不知道的是，每一份简历都是通过一个基于成本而非质量选择的模型生成的。
Why models sound generic in the first place
为什么模型最初听起来就很通用
LLMs predict the next likely token. Without strong, specific input, “likely” collapses to “common,” and common resume language is buzzword-heavy by default. The model can’t invent your impact. It only knows what you feed it, and when you feed it little, it reaches for legacy filler like synergy, stakeholder management, and team player. 大语言模型预测的是下一个可能的词元（token）。如果没有强大且具体的输入，“可能”就会坍缩为“常见”，而常见的简历语言默认充斥着流行语。模型无法凭空捏造你的影响力。它只知道你喂给它的内容，当你喂给它的信息很少时，它就会求助于“协同效应”、“利益相关者管理”和“团队合作者”这类陈旧的填充词。
That’s also why AI text feels weird even when it’s fluent. It’s abstract. It describes categories of work (“cross-functional collaboration”) instead of the concrete thing you did (“ran weekly syncs between design and backend to unblock the checkout redesign”). Recruiters read that abstraction as evidence you’re hiding a thin story, whether or not that’s true. 这就是为什么 AI 生成的文本即使流畅，读起来也很奇怪的原因。它太抽象了。它描述的是工作类别（“跨职能协作”），而不是你做的具体事情（“每周组织设计与后端同步会议，以扫清结账页面重构的障碍”）。招聘人员会将这种抽象视为你内容空洞的证据，无论事实是否如此。
The 3 dead giveaways of a generic AI resume
AI 通用简历的 3 个致命破绽
Recruiters spot AI resumes fast because the tells are consistent. Here are the three that matter most, and what each one signals. 招聘人员能迅速识别出 AI 简历，因为破绽非常一致。以下是三个最重要的破绽及其所暗示的问题。
1. Stock phrases with no proof behind them 1. 没有证据支撑的套话
The clearest giveaway is buzzword-heavy phrasing with nothing to back it up. “Results-driven professional with a proven track record” is a claim, not evidence. A human writes “cut checkout errors 30% by rebuilding form validation.” One asserts. The other shows. The problem isn’t that the words exist. It’s that people stop at that generic layer and never add the proof. Once a resume leans on stock phrases without a number, a decision, or a specific project, it reads as filler. 最明显的破绽是充斥着流行语却没有任何支撑。所谓“结果导向型专业人士，拥有经证明的过往业绩”只是一个声明，而非证据。人类会写“通过重构表单验证，将结账错误率降低了 30%”。前者在断言，后者在展示。问题不在于这些词本身，而在于人们停留在通用层面，从未添加证据。一旦简历依赖于没有数字、决策或具体项目的套话，它就会被视为填充内容。
2. Padded or invented metrics 2. 充水或捏造的指标
Cheap models love round, vague numbers because they sound impressive and cost nothing to generate. “Increased efficiency by 40%” with no baseline, no timeframe, and no method is a padded metric. Recruiters have read thousands and discount every one. Real metrics have texture: what you measured, over what period, and how. “Reduced average API response time from 800ms to 210ms over one quarter by adding caching” is believable because it’s specific. If your AI resume is full of clean percentages you can’t defend in an interview, that’s a tell and a risk. 廉价模型喜欢整数和模糊的数字，因为它们听起来令人印象深刻且生成成本为零。“效率提高了 40%”但没有基准、时间框架和方法，这就是充水指标。招聘人员读过成千上万份简历，对这类指标一律打折扣。真实的指标是有质感的：你测量了什么、在什么时期、以及如何测量。“通过添加缓存，在一个季度内将平均 API 响应时间从 800ms 降低到 210ms”是可信的，因为它很具体。如果你的 AI 简历里充满了你在面试中无法自圆其说的整齐百分比，这就是一个破绽，也是一种风险。
3. Identical structure and overly formal tone 3. 相同的结构和过于正式的语气
AI-written resumes tend to follow the same skeleton: a templated summary, an oversized skills section, then bullets that all start with the same handful of verbs. The language is formal and abstract, with no personal voice and no sense of the decisions you made. Modern applicant tracking systems (ATS) like Workday, Greenhouse, and Lever already parse a normal resume without that padding. So the bloated skills section and interchangeable summary aren’t helping you pass filters. They just make you look like everyone else who used the same tool. AI 编写的简历往往遵循相同的骨架：模板化的总结、过大的技能部分，以及都以同样的几个动词开头的要点。语言正式且抽象，没有个人风格，也没有体现你所做的决策。现代申请人跟踪系统（ATS，如 Workday、Greenhouse 和 Lever）已经能够解析普通的简历，无需这些填充。因此，臃肿的技能部分和可互换的总结并不能帮你通过筛选，只会让你看起来和所有使用同一工具的人一样。
Why one prompt isn’t enough for a tailored resume
为什么单一提示词不足以定制简历
A single prompt can’t tailor a resume properly because tailoring is several different jobs, and one pass does none of them well. When you paste your resume and type “tailor this to the job,” the model tries to read the posting, find your matching experience, decide what to emphasize, and rewrite everything, all in one shot. It ends up doing each step shallowly. The most common failure is that the model only loosely incorporates the actual job description. You get bullets that are broadly plausible for the role but not tightly aligned to its specific needs. 单一提示词无法妥善定制简历，因为定制涉及多项不同的工作，而一次性处理无法做好其中任何一项。当你粘贴简历并输入“根据职位进行定制”时，模型会尝试阅读职位描述、寻找匹配的经验、决定强调重点并重写所有内容，这一切都在一次请求中完成。结果就是每一步都做得浮于表面。最常见的失败是模型只是松散地结合了实际的职位描述。你得到的要点对于该职位来说大致合理，但并没有与具体需求紧密对齐。