63 lines
4.1 KiB
Markdown
63 lines
4.1 KiB
Markdown
# LinkedIn Draft & Consultant Review: Stop Writing Agent Prompts for Deterministic Work
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> **文档性质**：从 `blog.hoelee.com` 改写并在 NocoDB 排期的 LinkedIn 生产级动态。
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> **发布状态**：`已通过外部顾问审阅 / NocoDB 已排期待发布`
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> **排期波次**：Wave 2 (纵深期) - 槽位 8 (拟发布日期: 2026-11-24 Tue)
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> **源文章**：https://blog.hoelee.com/posts/stop-writing-agent-prompts-for-deterministic-work/
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> **系统标识**：`stop-writing-agent-prompts-for-deterministic-work` | **分类**：`notes`
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> **字数**：1959 chars (严格控制在 3,000 字符内)
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---
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## 一、 核心背景与铁律约束提示（供复核对照）
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1. **求职画像契合（Senior 全栈/DevOps/AI 架构师）**：彻底脱敏“做生意的老板/小公司创始人”倾向；一切商业实战均转换为“生产系统架构、高可用交付与自动化运维”视角。
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2. **纯洁人设与零禁用词**：绝对禁止 `homelab`, `home lab`, `家用`, `小主机`, `玩具`, `折腾`, `玩票` 等词汇。必须使用 `the infrastructure I manage`, `my self-hosted production nodes`, `production host`。
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3. **第一人称单数**：统一使用单数 `I` / `我`，杜绝虚张声势的复数 `we`。
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4. **数据绝对保真**：所有数字、性能比率、吞吐提升、故障现象严禁凭空捏造。
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5. **OPSEC 安全脱敏**：无内部绝对路径（如 `D:/dev`），无真实私有内网 IP，公网 IP 必须转为文档保留地址。
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---
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## 二、 LinkedIn 拟发布动态文案（已按顾问意见修订）
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```text
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One of my automated background jobs failed 29 times in a row without sending an alert.
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The core check was trivial: query npm for the latest published version of a CLI tool, compare it with the currently installed version, and send a notification if a newer version exists.
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Two strings and a version inequality.
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Because I already had an autonomous agent scheduler running daily ops summaries, I lazily wired this check as an agent prompt task instead of writing a dedicated script. Every cycle, an LLM parsed the CLI output and decided whether to alert. It worked fine until an upstream model routing change occurred. The agent framework recorded a model mismatch, triggered a silent drift skip, and exited cleanly without error.
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The fix was not a more defensive prompt. It was deleting the LLM from the loop entirely.
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The rule I enforce across all automated pipelines now:
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**If the exit condition is a deterministic comparison — a string, a number, a status code — putting a model in that loop only adds failure modes.**
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There is zero judgment required in `currentVersion < latestVersion`. Wrapping it in an LLM adds:
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• Network latency and provider downtime
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• Model drift and prompt deprecation risks
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• Non-deterministic output formatting
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• Unnecessary token billing
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I replaced the agent task with a deterministic 125-line Python script handling HTTP retries, state deduplication, and explicit exit codes. It has run cleanly ever since.
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Of the eleven automated operations loops I manage today, eight contain zero AI models in their execution path. The remaining three use LLMs exclusively where human ambiguity cannot be expressed as code: ranking unstructured emails, evaluating qualitative risk, and flagging stalled project contexts.
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Where in your workflows are you using prompt engineering where a deterministic script would be infinitely more resilient?
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Write-up: https://blog.hoelee.com/posts/stop-writing-agent-prompts-for-deterministic-work/
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```
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---
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## 三、 外部顾问审阅评估反馈单与归档记录
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- [x] **审阅结论**：需微调（已更新修订稿）
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- **1. 诊断与修改批注**：防杠微调：交代最初挂在调度器背景与 125 行包含重试/去重/退避逻辑，立住工程克制力形象。
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- **2. NocoDB 归档记录**：
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- Candidate Key: `cand:stop-writing-agent-prompts-for-deterministic-work` (Status: `APPROVED`, Priority: `HIGH`)
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- Draft Key: `cand:stop-writing-agent-prompts-for-deterministic-work:v1` (Status: `APPROVED`)
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- 拟定发布节奏：每周 2 篇（周二/周四），平稳推进 5 周。
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