2026 年，大模型的能力还在持续提升，但有一件事始终没变——算力永远追不上需求。更关键的是，用 AI 写代码这件事，能不能用好、能不能写对，根本不取决于模型有多强，而取决于你有多懂。很多人以为用了 AI 写代码就能「躺赢」，结果却撞上最坏的情况：遇到 bug 不知道怎么修，连问题出在哪都看不出来。
本文不说空话，直接回答三件事：为什么算力追不上（现状）、领域专家凭什么一眼看出问题（专业能力）、怎么把专家经验变成 AI 听得懂的提示词（方法论），并给出一段可以跑的对照代码。
📦 配套资源：本文为观点分析类，无源码包、配置清单或图表源文件，配套资源项为空，跳过开篇资源位。
目录
- 拼到最后拼的是人：一个反常识的结论（The Human Factor）
- 算力永远跟不上：瓶颈的底层逻辑（Compute Bottleneck）
- 最坏的情况：不懂技术的人用 AI 写代码（The Worst Case）
- 领域专家凭什么一眼看出问题（Expert's Eye）
- 专业提示词：把专家经验注入 AI（Professional Prompts）
- 症状 → 原因 → 解法对照表（Symptom-Cause-Solution）
- 一段可运行的对照代码：新手 prompt vs 专家 prompt（Runnable Code）
- 什么时候该用、什么时候不该用（Judgment）
1. 拼到最后拼的是人：一个反常识的结论（The Human Factor）
先说结论：AI 写代码，拼到最后拼的还是人。 不是模型，不是算力，是人的专业能力。
这一年下来，大模型的能力一直在提升，但有一点始终没变——「算力永远跟不上」。你会发现一个很扎心的现实：当模型能力上去了，卡的是硬件；当硬件勉强跟上了，卡的又是人的使用水平。AI 工具再强，它也只是放大镜，你本身有多专业，它放大的就是多大的专业；你不专业，它放大的就是你的盲区。
最典型的就是「vibe coding（氛围编程）」带来的幻觉。现在很多人用它几分钟就能「写」出一个 App，看起来很爽，但这种爽是有代价的。安全研究机构对 5600 多个 vibe coding 应用扫描后发现：超过 2000 个安全漏洞、400 多个暴露的密钥、175 例个人隐私数据泄露。能跑，不代表能上线；能用，不代表懂。 这就是「拼的是人」这句话最直接的注脚。
为什么是这个结论？因为 AI 写代码的本质，是你问什么、它给什么。你问不出高质量的问题（提示词），它就给你平庸的答案；你分不清它给的答案对不对，你就只能照单全收，然后被埋在它埋下的坑里。低垂的果实（low-hanging fruit）被摘完之后，剩下的每一寸进步都靠人。
这个主题，其实能在本专栏前序文章里找到呼应。在agent 为什么需要自进化那篇里我们讲过，agent 光靠参数堆砌会「训练完就静止」，必须靠经验机制才能持续变强——而经验恰恰来自懂行的人如何设置反思、如何沉淀策略。模型的瓶颈要靠机制补，机制的瓶颈要靠人来设计。
2. 算力永远跟不上：瓶颈的底层逻辑（Compute Bottleneck）
为什么说「算力永远跟不上」？这不是悲观，而是结构性事实。
需求端在指数级膨胀。 中国信通院数据显示，截至 2026 年 6 月，我国日均 Token 调用量已突破 500 万亿，相比 2025 年末的 100 万亿大幅提升。模型能力越强、智能体（agent）越普及，调用量就越大，对算力的渴求就越无限。
供给端被多重瓶颈卡死。 国家发展改革委宏观经济研究院研究员张林山指出，生成式 AI 应用的井喷催生海量算力需求，科技巨头与初创企业纷纷投身「百模大战」，导致对高性能 GPU 的需求暴涨；而供给端受产能、先进制程、高端卡进口限制等多重约束，缺口持续扩大。「算力价格上涨，根本原因是供需失衡」，赛智产业研究院智能经济研究所所长李铭岩说得直白。
架构本身也在成为瓶颈。 中国信通院《AI 计算节点发展研究报告（2026 年）》指出，为应对千亿、万亿参数大模型训练对通信性能的极致要求，传统以服务器为单元、依赖外部网络堆叠的算力架构，已成为制约算力效能释放的主要瓶颈。当前 AI 计算节点的演进主线正在从「以连接 CPU 为中心」转向「以 GPU 互联为中心」，靠架构重构来挤效率。
把这层逻辑翻译成人话：算力就像路上的车，需求是车流，供给是车道。车流年年翻倍，车道却在被各种路障（产能、制程、限购）卡着。 所以别指望哪天算力突然管够，那既不现实，也不是解决问题的关键。真正能撬动效率的杠杆，不在硬件上，而在「你怎么用」。
算力瓶颈参考资料：
- 算力涨价潮影响与应对 -- 新华网 ⭐值得阅读
- 大模型「狂飙」之下 国产算力缺口如何补齐 -- 中国高新技术产业导报 ⭐值得阅读
- AI 计算节点发展研究报告 (2026 年) -- 中国信通院
- 算力短缺，巨头疯抢「超节点」 -- 钛媒体
3. 最坏的情况：不懂技术的人用 AI 写代码（The Worst Case）
如果说算力瓶颈是「硬约束」，那「不懂技术还硬写」就是最容易被忽略的软灾难。用 AI 写代码最坏的情况是什么？是你根本就不懂这个技术，告诉 AI 不知道怎么写，它写完你也看不懂。 一旦它埋了个 bug，你能做的事只有干瞪眼——修它一整个下午，你都不知道从哪下手。
这就是「vibe coding」最残酷的一面。它让「写代码」的门槛降到了前所未有的低，但调试（debugging）的门槛一点都没降。你可以让 AI 五分钟「写」出一个 app，但当它跑出奇怪的行为时，你没有能力判断「这是幻觉依赖、是状态没重置、还是逻辑根本错位」。
前面第 1 章提的安全数据已经说明问题：5600 多个 vibe coding 应用，扫出 2000+ 个漏洞、400+ 个暴露密钥。这些应用的创建者大多不具备安全知识。他们不是故意埋雷，是根本看不见雷。
不懂行的人踩的坑，往往集中在几类：
一是「幻觉依赖（hallucinated dependencies / slopsquatting）」。 AI 会编出看起来很像真的包名和方法名。懂行的人第一反应是「去 lockfile 里核对这个 import 到底存不存在」；不懂行的人直接 pip install，于是装上了攻击者抢注的同名恶意包。
二是「做对了结构，做错了意图」。 一个很典型的例子：AI 写的 try...catch 块里，catch 里只 console.log(error)，然后什么都不做——没有 fallback、没有提示用户、没有重试。代码不崩溃，只是「安静地坏掉」。外行看起来一切正常，只有懂意图的人知道这功能已经废了。
三是「反向追因」无从谈起。 真正的 debug 高手，是从症状倒着往回找（trace backwards from the symptom）：「第二次上传绕过了弹窗」→ 从上传执行点开始问「是什么条件让它跑了」→ 找到那个没被重置的标志位。这套逆向推理的前提，是你脑子里有一个「正确的系统长什么样」的模型。 你没有这个模型，AI 再聪明也救不了你。
把这三类坑连起来看，会得出一个很扎心的结论：AI 降低了「写出代码」的门槛，却丝毫没有降低「读懂代码、排错代码」的门槛。 于是那些只享受了前半段红利的人，会结结实实地摔在后半段上。门槛并没有消失，它只是从「写」转移到了「判断」上。
最坏情况参考资料：
4. 领域专家凭什么一眼看出问题（Expert's Eye）
前面讲的是「不懂的人有多惨」，这一章讲另一面——懂的人凭什么能一眼看出问题。这恰恰是「拼到最后拼的是人」这句话的核心论据。
有个很生动的真实案例：年轻工程师用 AI 写代码，遇到 bug 的第一反应是「把报错信息丢给 AI 分析」；而一个只会「古法编程」的老工程师，第一反应却是 「先猜机器哪里不舒服」。他知道一个看似随机的崩溃，可能是因为某个寄存器的某个位没置位；一次通讯失败，可能是线缆过长导致信号衰减；一个数据错乱，可能是中断嵌套太深导致栈溢出。他脑子里装着一整套「机器在想什么」的模型，所以他能从蛛丝马迹里反推出真正的病因——而这是 AI 和报错文本都给不了你的。
为什么 AI 工具做不到这一点？因为 AI 擅长的是「模式识别（pattern recognition）」，而不是「意图理解（intent-level understanding）」。 各类 AI code review 工具的实测都指向同一个结论：它们能稳定抓出符合已知模式的错误（未用变量、不可达代码、潜在空指针、语言特定的反模式），但抓不住没有模式可循的错误——业务逻辑错位、AI 幻觉出的认证流程、跨服务越权、高并发下的竞态条件（race condition）。
换句话说，AI 是「扫描仪」，只能看到表面；专家是「医生」，能根据症状推断体内发生了什么。 扫描仪扫不到的那种病，才真正要命。这也是为什么几乎所有关于 AI 代码审查的建议都会补一句：自动工具无法检测意图级错误，对 AI 生成的代码，专家人工审查不可替代（non-negotiable）。
而资深工程师在 AI 时代并没有被淘汰，只是转岗了。AI 接管了机械性的检查（风格违反、空指针、文档缺失）之后，资深开发者把精力转移到更高价值的事上：架构审查（这符不符合我们的系统设计）、安全分析（这个领域特有的威胁是什么）、性能评估（它能扛住规模吗）、以及带教（告诉新人为什么 AI 的这条建议很重要）。这四件事，恰恰是 AI 目前最不擅长的。
专家能力的来源，可以拆成三个层面：
- 领域模型（mental model）：脑子里有「系统理应如何运转」的完整图景，所以能判断「正常」与「异常」的边界。
- 经验指纹（pattern of failure）：踩过足够多的坑，知道「这类问题通常长什么样、藏在哪里」。
- 归因能力（root-causing）：能从表象逆推根因，而不是被表象牵着走。这三层，靠的都是人的长期积累，不是模型参数能替代的。
专家眼力参考资料：
5. 专业提示词：把专家经验注入 AI（Professional Prompts）
前面说了「专家有一双慧眼」，那怎么把这双慧眼「借给」AI？答案是——把专家的经验、约束、验收标准，写进提示词（prompt）里。提示词就是你递给 AI 的任务说明书，说明书写得多专业，AI 的产出就有多专业。
先看一个扎心的量化事实。有数据显示，84% 的开发者已经在用 AI 工具，但只有不到 30% 的人能稳定获得可用于生产的代码。原因不是模型不好，而是模糊的提示词只能产出平庸的代码——大多数开发者把 AI 当 Google 用，直接丢一句「write a function to sort an array」，然后抱怨结果像 2010 年的 StackOverflow 答案。
还有一个更直观的对比：模糊描述生成的代码可能需要 80% 的返工，而结构化提示词能把一次通过率提升到 80% 以上。同一个模型、同一个需求，提示词写得清不清晰，能差出好几倍的效率——这不是玄学，是「把话讲清楚」的工程学。
那「专业提示词」到底长什么样？它并不是什么神秘咒语，而是包含六个要素的结构化任务说明书：
这份表格的本质，是把专家脑子里那些「不言自明」的隐性知识，翻译成 AI 能听懂的统一语言。专家写提示词快，不是因为他手速快，而是因为他知道该取舍什么、该禁止什么、该用哪个方案、什么才算「对」。这些判断，恰恰是新手最缺、也最值钱的部分。
把专家经验注入提示词，可以归纳成三个可复用的动作：
- 给具体目标，别给指令。不要只说「优化一下」，要说「把接口响应耗时降到 P95 < 200ms」。可衡量的验收标准能逼 AI 产出可判断的结果。
- 给约束，尤其给「不要什么」。明确「不引入新依赖」「不改签名」「不删字段」，能阻止 AI 越界发挥，也防止它膨胀出你不想要的东西。
- 给环境与示例。告诉它「Linux + 有复现日志」，或者给一个 few-shot 例子，让 AI 对齐你的业务语境，而不是套用通用模板。
一句话总结这一章：提示词不是玄学，是「把话说清楚」的工程。 你能把需求讲多清楚，AI 就能把活干多好——而「讲清楚」这件事，靠的是专业知识，不是打字速度。
专业提示词参考资料：
6. 症状 → 原因 → 解法对照表（Symptom-Cause-Solution）
前面五章拆开了「算力瓶颈」「不懂的坑」「专家的慧眼」「提示词的方法」，这一章把「你会在 AI 写代码时撞到的坑」和「怎么治」捋成一张可以对照的表。如果你正在用 AI 写代码，照着症状对号入座即可。
这张表想传达的判据是：绝大多数「AI 写代码翻车」的坑，根子都在「人的判断力不足」，而不是「模型不够强」或「算力不够」。所以解法优先级永远是「先提升人，再优化工具」。
对照表参考资料：
- Vibe Coding 离生产级应用有多远 -- 人人都是产品经理 ⭐值得阅读
- Vibe Coding Explained: Risks and Best Practices -- Artificial Intelligence +
- Vibe Coding in 2026: Speed, Risks, Production Rules -- FutureAGI
7. 一段可运行的对照代码：新手 prompt vs 专家 prompt（Runnable Code）
前面全是理论，这一章给一段可以直接跑的代码，演示「新手 prompt」和「专家 prompt」让同一个 AI 输出同一段功能时的差别。它用一个极简的 CodeGenerator 模拟「提示词 → 生成代码 → 质检（返工判断）」的流程。
# -*- coding: utf-8 -*-
"""
AI 写代码「新手 prompt vs 专家 prompt」的最小对照 demo：
同一个模型、同一个需求，只差提示词质量，产出与一次通过率天差地别。
本 demo 只用标准库，无需额外安装依赖。
"""
def generate_code(prompt):
"""按提示词质量模拟 AI 生成的代码（这里用提示词质量打分近似返工率）。"""
# 模拟：模糊提示词 → 生成代码需要大量返工；结构化提示词 → 一次通过
if "验收标准" in prompt and "约束" in prompt and "输出格式" in prompt:
return "一次性通过，代码可直接上生产", 0.0 # 返工率 0%
return "代码能跑但需返工，存在安全隐患", 0.8 # 返工率 80%
def novice_prompt():
"""新手 prompt：模糊指令，缺上下文与约束。"""
return "帮我写一个排序函数"
def expert_prompt():
"""专家 prompt：结构化任务说明书，含六要素。"""
return (
"你是一名资深 Python 后端工程师。"
"任务：为百万级订单的 REST API 实现一个稳定的排序函数。"
"约束：Python 3.12，不引入新依赖，不改函数签名。"
"输出格式：返回 JSON，字段为 code/message/data。"
"验收标准：pytest 全绿 + 接口耗时降低 30%。"
)
if __name__ == "__main__":
# 模拟同一个模型分别接收两类提示词
print("===== 新手 prompt =====")
novice_result, novice_rework = generate_code(novice_prompt())
print(f"AI 产出: {novice_result}，返工率: {novice_rework * 100:.0f}%")
print("\n===== 专家 prompt =====")
expert_result, expert_rework = generate_code(expert_prompt())
print(f"AI 产出: {expert_result}，返工率: {expert_rework * 100:.0f}%")
print("\n结论：同一个模型，提示词写得清不清晰，返工率可从 80% 降到 0%。")
代码想说明什么？ 核心是 generate_code 里的那一行判断——「验收标准」「约束」「输出格式」三个词在不在提示词里，直接决定了结果是一次通过还是 80% 返工。生产环境里，你会把这个函数换成真实的模型调用 + 测试套件 + 专家 review，原理完全一样：专业提示词，是把专家的隐性知识前置到 prompt 里。
代码部分参考资料：
8. 什么时候该用、什么时候不该用（Judgment）
哪些情况该靠「专业能力 + 提示词」这条路？ 当你要用 AI 写自己真正懂的领域代码时——你清楚边界、知道该禁止什么、能判断对错。此时专业提示词能大幅放大你的效率，AI 是你的超级放大器。同样，当你面对可复用、可验证的任务时，用结构化提示词能显著降低返工率。
哪些情况不该急着上 AI 写代码，或者不该只靠 AI？ 当你对某个领域完全不了解时，别幻想 AI 能替你「跳过学习」。你可以让 AI 帮你搭骨架、做 demo，但核心逻辑、安全方案、业务意图必须由懂行的人把关。另外，在高风险、强审计的场景（金融核心链路、涉及真实用户数据的后端），专家人工审查不可替代，AI 生成的代码必须过专家这一关才能上线。
一句话判据：「懂行 + 可验证」才值得用 AI 放大效率；「不懂 + 高风险」先别碰，AI 救不了你。 拼到最后拼的还是人，不是一句口号，是你在键盘前每敲一个 prompt 时的真实处境——先看自己懂不懂，再看任务风险高不高，再决定怎么用 AI。
专栏介绍：本专栏《LLM 大模型技术专栏》从 Transformer 注意力机制一路讲到 LangChain、RAG 与 MoE 落地，每篇都配一段可直接运行的代码，每周更新一篇。想按顺序读完，可在专栏页订阅。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
In 2026, the capacity of LLM continued to improve, but one thing remained unchanged — the ability to keep up with demand. And the more important thing is, whether it's good, whether it's good, whether it's right, depends not on how strong the model is, but how well you know. A lot of people think that they're going to be able to "slept" with an AI code, but they hit the worst case: having a bug that doesn't know how to fix it, can't even see where the problem is.
Without saying anything, this paper answers three things: why is it impossible to catch up (the current state of affairs), how do experts in the field see the problem at once (professional competence), how do they turn their experience into AI's hearable prompt (methodology) and give a running contrast code?
• Accompanying resources: This is a view analysis category, a non-source package, a configuration list or a chart source document, a resource package that is empty and skips the opening resource slot.
Contents
- It's the man at the end: the human factor.
- Countless never follows: Bottom logic of bottlenecks
- Worst case scenario: people who don't know how to write codes in AI
- How can a field expert see a problem?
- Professional prompt: Influencing expert experience
- Symptom-Cause-Solution
- A runable control code: beginner prompt vs expert prompt
- When to use, when to use.
1. It's the man at the end: an anomalous conclusion.
Let's start with the conclusion: AI wrote the code, and it was the man who spell it. It's not a model, it's not a calculus, it's human expertise.
LLM's capacity has been improving since the year, but one thing remains unchanged — “calculations never keep up”. You'll find a very solid reality: when the power of the model goes up, the card is hardware; when the hardware is barely up to date, the card is human. It's just a magnifier. You're a pro. It's a pro.
Most typical is the illusion of "vibe coding." Now a lot of people can "write" an app in a few minutes. It looks good, but there's a price to it. The security research institute scanned more than 5600 vibe coding applications and found more than 2,000 security holes, more than 400 exposed keys and 175 personal privacy data leaks. Can run, doesn't mean you can go online; can work, doesn't mean you can understand. This is the most direct footnote to the phrase "manhood."
Why that conclusion? Because the essence of AI writing code is what you ask, what it gives. And when you cannot ask a high-quality question (prompt), it gives you a plain answer; and you cannot tell the answer from it, can you not? You can only take it all and then be buried in the pit it buried. After the low-hanging fruit has been removed, every inch of progress remains to be made.
This is a theme that can actually be answered in the preface to this column. In the story about why angent needs to evolve, we said that angent alone has to be "still" with a stack of parameters, and it has to be done with an empirical mechanism that keeps getting stronger -- experience that comes from people who know how to think, how to sink. Model bottlenecks need to be filled by mechanisms that are designed by people.
2. Calculus never follows: Bottom logic of bottlenecks (Compute Bottleneck)
Why do you say "calculation never keeps up"? This is not a pessimism, but a structural fact.
Demand-side expansion at the index level. According to the Chinese Institute of Information and Communication Technology, as of June 2026, our daily average number of Token calls had exceeded 500 trillion, up significantly from 10 trillion at the end of 2025. The stronger the model, the more AI agent (agent) becomes, the greater the call, and the greater the thirst for computing.
The supply side was killed by multiple bottlenecks. According to Zhang Linshan, researcher at the Macroeconomic Research Institute of the National Development and Reform Commission, the production of AI-based well blow-outs to generate large-scale computing needs and the investment of technology giants and start-ups in the "100-mold war" have led to a surge in demand for high-performance GPUs, while supply-side constraints on production capacity, advanced processing and high-end card import restrictions continue to widen. "The rise in the price of computing power is essentially due to the imbalance between supply and demand", as stated by the Director of the Institute of Smart Economics of the Institute of Intellectual Industries of Saicai.
The architecture itself is becoming a bottleneck. The AI Node Development Study (2026) of the Chinese Institute of Information and Communication Technology states that, in response to the extreme demand for communication performance for the hundreds and hundreds of billions of parameters LLM training, the traditional server-based algorithm that relies on the stacking of external networks has become a major bottleneck in limiting the release of computing power. The current evolution of the AI calculation node is moving from "connecting CPU" to "gPU interconnection" to "regulating" by reconfiguring the structure to squeeze efficiency.
Translating this logic into humans: math is like a car on the road, the demand is traffic, the supply is the driveway. The flow doubles year by year, while the driveway is stuck by various roadblocks (capable, routed, restricted). So don't count on a day when the math will suddenly be enough, which is neither realistic nor the key to solving the problem. It's not about hardware, but about how you use it.
Reference to factor bottlenecks:
It's worth reading.
Under LLM, how do we close the gap in national production?
AI Study on the development of computational nodes (2026) -- China ICT Hospitals
- We're running out of numbers, giants are crazy about super nodes - titanium media
3. Worst case scenario: write code (The World Case) for those who do not understand technology
If the measuring bottlenecks are "hard constraints", then "not knowing how to spell" is the softest disaster to be ignored. What's the worst thing about writing codes with AI? You're the one who doesn't understand this technology. Tell AI you can't read it. Once it's buried a bug, all you can do is stare. - You didn't know where to do it all afternoon.
This is the cruelest aspect of "vibe coding". It brought the "writing code" threshold down to an unprecedentedly low level, but the debugging threshold did not fall at all. You can get AI to "write" an app for five minutes, but when it runs out of strange behavior, you have no ability to judge whether it's an illusion dependent, a state unreseted or a logical error.
The safety data referred to in chapter 1 above have shown the problem: more than 5600 vibe coding applications, closing the 2000+ loophole and 400+ exposure keys. Most of the creators of these applications do not have safety knowledge. They didn't do it on purpose. They couldn't see it.
The pits used by those who do not know how to do it tend to be concentrated in several categories:
One is "hallucinated dependence / slopsquatting." AI will make up a name that looks like a real package and method. The first reaction of those who know how to do it is "to check the existence of this report in the lockfile"; those who don't know how to do it directly pip install, and then put in the same name as the attacker.
The second is "doing the right structure, doing the wrong intention." A typical example: AI wrote a try... catch section, which is only console.log, and does nothing -- no fallback, no hint to users, no retry. The code didn't collapse, it was just "silently broken." An amateur seems to be doing all right, and only those who know what they're doing know it's gone.
The third is that there is no "reverse pursuit" to talk about. The real debug master is looking backwards from symptoms: "second upload bypassed the bullet window" and started at the upload implementation point asking, "What conditions made it run away" to find the unreplaced mark. The premise of this reverse reasoning is that you have a model of what the right system looks like. You don't have this model, AI. You're smart enough to save you.
The combination of these three types of pits leads to a very strong conclusion: AI lowers the threshold of "writing the code" but does not lower the threshold of "reading the code and putting it wrong." Those who only benefited from the first half of the dividend fell on the second half. The threshold did not disappear; it simply moved from writing to judging.
Worst-case references:
How can experts in the field see the problem at once (Expert's Eye)
What I said earlier was, "How bad is it for people who don't know" and this chapter says the other side — who knows how to see things at once. This is precisely the central argument of the phrase "it's the man who fights at the end."
There is a very vivid case: young engineers write codes using AI, and the first reaction to bugs is to "send false information to AI analysis"; and an old engineer who only "programs ", the first reaction is to "pregnate where the machine is uncomfortable." He knew that a seemingly random collapse might be due to the absence of a certain part of a repository; that a failure of communication could be due to the loss of a signal as a result of the excessive length of the cable; and that a data anomaly could be due to the spilling out of the dam as a result of the interruption of the nest too deep. He's got a whole set of models in his head about what the machine is thinking, so he can get the real cause out of the trail -- and this is AI and the wrong text.
Why can't AI tools do that? Because AI is good at "pattern recognition", not "intent-level understanding." The results of the various AI code review tools point to the same conclusion: they can stabilize errors that meet known patterns (unused variables, inaccessible codes, potential blanks, language-specific counter-models), but they cannot capture errors that do not have a model to follow - business logic errors, AI hallucinating authentication processes, ultra-service, high-level competitive conditions.
In other words, AI is a "scanner" and can only see the surface; the specialist is a "doctor" who can infer what's happening in the body based on symptoms. It's the kind of disease that can't be cleaned up by a scanner. This is why almost all of the suggestions for an AI code review add that an automated tool cannot detect an intention-level error and that an expert manual review (non-negotiable) is not a substitute for an AI-generated code.
And the senior engineer wasn't out in the AI era, just transferred. AI, after taking over mechanical inspections (style violations, empty compasses, missing files), the senior developer shifted his energy to something of higher value: a structural review (which is not in line with our system design), a security analysis (what are the threats unique to this area), performance assessment (can it carry the scale) and teaching (which is important to tell the newcomer why AI). These four things are exactly what AI is least good at right now.
The sources of expert capacity can be broken down into three levels:
- Area model (mental model): The full picture of how the system should work is in mind, so it can judge the boundary between normal and unusual.
- Empirical fingerprinting (pattern of justice): overcame enough pits to know what such problems usually look like and where they are hidden.
- Attribution capability (root-causing): can reverse the root cause from the appearance rather than being taken away from it. These three layers are based on the long-term accumulation of human beings, not on model parameters.
Expert eye reference:
5. Professional prompt: Infusion of expert experience
How do you lend them to AI? The answer is - the expert's experience, constraints, acceptance criteria, written in prompt (prompt). prompt is the mission statement you sent to AI, which shows how professional it is, and how professional it is.
Let's start with a solid quantization fact. Data show that 84% of developers are already using AI tools, but less than 30% have steady access to codes that can be used for production. The reason is not that the model is bad, but rather that the fuzzy prompt can only produce plain code -- most developers throw an AI as Google uses a "write a play to sort an answer" and complain about results like the 2010 StackOverflow answer.
There is also a more intuitive contrast: vague descriptions of the resulting code may require 80% return work, and structured prompt can increase the pass rate to more than 80%. The same model, the same demand, the prompt is not clear, it's multiple times more efficient. - It's not grammatics, it's engineering.
What exactly does prompt look like? It is not a myth but a structured mission statement containing six elements:
The essence of this form is to translate into a uniform language that is understood by AI, the hidden knowledge of the expert's mind. The expert wrote prompt fast, not because he was quick, but because he knew what to choose, what to forbid, what to use and what to say. These judgements are precisely the least and most valuable part of beginner.
Injecting expert experience into prompt can be summarized as three reusable actions:
- Give specifics, no instructions. Not just "optimize" but "delay the interface response to P95 < 200ms". Measurable acceptance criteriaes can push for measurable AI outputs.
- To be restrained, especially to "no." The clear "no new reliance" "no signature" "no field" will stop AI from spreading across borders and prevent it from swelling what you don't want.
- Give environment and examples. Tell it "Linux + has a recurrence log" or give a new-shot example to align AI with your business context instead of applying a generic template.
One sentence summed up this chapter: prompt is not an encyclopedia, it's a project to make things clear. You can tell the need how well AI can do the job -- and the "clear" thing depends on expertise, not typing speed.
Professional prompt reference:
Symptom-Cause-Solution
The first five chapters, which remove the "ununderstood pit" "the expert's good eye" "prompt'", make a comparison between "the pit you'll hit while writing codes in AI" and "how to treat". If you're using AI to write code, you can just sit on the symptoms.
This table is intended to convey the verdict that most of the pits of "AI writing the code over the car" have roots that are "under-judgement" rather than "inadequate models" or "inadequate math". So the solution priority is always "to promote people, then to optimize."
References to the table:
- Vibe Coding, how far from the production level -- everybody's a product manager.
- Vibe Coding Explained: Risks and Best Practices -- Artificial Intelligence +
- Vibe Coding in 2026: Speed, Risks, Production Rules -- FutureAGI
7. A runable control code: beginner prompt vs expert prompt
This chapter gives a code that can run directly, demonstrating the difference between "beginner prompt" and "expert prompt" when one AI output the same paragraph. It uses a very simple CodeGenerator to simulate the process of "prompt → to generate code certifier" (re-entry judgement).
# -*- coding: utf-8 -*-
"""
Minimum contrast of AI writing code "beginner prompt vs expert prompt"
The same model, the same demand, is short of the quality of prompt, and outputs differ from a single pass rate.
This demo only uses a standard library and does not require additional installation dependency.
"""
def generate_code(prompt):
"""at prompt Mass Simulates the code created by AI. (Here prompt Mass marks the approximate return rate.) "'"
# Simulation: Fuzzy prompt # code generation takes a lot of work back; structured prompt # pass once
If "acceptance criteria" in policy and "constrained" in policy and "output format" in formula:
"One-time pass, code can be produced directly", 0.0 # return rate 0%
"Code can run but needs to return to work, there's a safety hazard," 0.8 # return rate 80 #
def novice_prompt():
""beginner policy: Fuzzy command, missing context and constraints. "'"
Turn, write me a sorting function.
def expert_prompt():
"Expert prompt: structured mission statement with six elements."
return (
"You're a senior Python back-end engineer."
Mission: achieve a stable sorting function for a million-grade REST API order.
"Constraint: Python 3.12, does not introduce new dependence, does not change a functional signature."
"Output format: returns JSON, field is code/messeage/data."
"acceptance criteria: pytest full green + interface timed down by 30%."
)
if __name__ == "__main__":
# Simulate the same model to receive two types of prompt
("===beginner prompt========)
novice_result, novice_rework = generate_code(novice_prompt())
print(f"AI output: {novice result}, return rate: {novice rework*100:.0f}%”)
{\cHFFFFFF}{\cH00FFFF}
expert_result, expert_rework = generate_code(expert_prompt())
Print(f"AI output: {ext result}, return rate: {ext rework*100:.0f}%”)
Point ("Conclusion: The same model, prompt, is not clear and the return rate can drop from 80% to 0%. " )
What does the code mean? At the core is the line of judgment in generate code -- the three words "acceptance criteria" "constrained" "output format" are not in prompt, which directly determines whether the result is a pass or 80% return to work. In the production environment, you will convert this function to a real model call + test package + specialist review, exactly the same principle: professional prompt, which pre-positions the specialist's hidden knowledge to the prompt.
Code Part References:
8. When to use and when not to use (Judgment)
What kind of situation should we rely on? And when you're going to use AI to write the field code you really understand -- you know boundaries, you know what to forbid, you can judge right or wrong. At this point in time, professional prompt can significantly increase your efficiency, and AI is your super amplifier. Similarly, when faced with reusable and verifiable tasks, a structured Z41Z can significantly reduce the return rate.
What kind of situation shouldn't rush into AI writing codes or just AI? When you're completely ignorant of something, don't fantasize that AI can "jump and learn" for you. You can get AI to help you build a skeleton and make a demo, but core logic, security options, business intentions must be closed by someone who knows how to do it. In addition, in high-risk, robust audit scenarios (core financial links, back end of real user data involved), there is no substitute for manual expert review, and AI generation codes must pass the expert level to go online.
One sentence says, "Know + Verifyable" is worth using AI to magnify efficiency; "Don't understand + High Risk" before it's too late. It's not a slogan, it's the real situation of you when you knock on every prompt in front of the keyboard -- see if you understand it or not, and then decide how to use AI.
The column " LLM Z43Z Technical column " refers all the way from Transformer ' s focus to Langchain, RAG and MoE, each with a direct running code, updated once a week. To read in order, you can subscribe to the column page.
Auto-fetched and translated by AI Atlas. All rights belong to the original author.