OpenAI 发布的职业提示词第四弹：工程相关提示词

我是一个基础设施工程师，正在评估云迁移选项。背景：我们将从本地迁移到云端，用于金融科技后端。输出：比较 AWS、GCP 和 Azure 的可扩展性、定价、合规性和开发者工具。包含引用。

I’m an infrastructure engineer evaluating cloud migration options. Context: We’re moving from on-prem to the cloud for a fintech backend. Output: Compare AWS, GCP, and Azure for scalability, pricing, compliance, and developer tooling. Include citations.

我正在开发一个实时协作工具。背景：我们需要低延迟和可扩展性。输出：比较顶级框架（例如，SignalR、Socket.io、WebRTC），包括用例、优缺点以及其他 SaaS 公司当前的使用情况。包含参考资料。

I’m building a real-time collaboration tool. Context: We need low-latency and scalability. Output: Compare top frameworks (e.g., SignalR, Socket.io, WebRTC) with use cases, pros/cons, and current usage by other SaaS companies. Include sources.

基准测试顶级可观测性工具。背景：我们希望从基本日志记录迁移到全栈监控。输出：创建一个比较 Datadog、New Relic、Prometheus 和 OpenTelemetry 的功能、定价和集成的表格。包含参考资料。

Benchmark the top observability tools. Context: We want to move from basic logging to full-stack monitoring. Output: Create a comparison table of features, pricing, integrations for Datadog, New Relic, Prometheus, and OpenTelemetry. Include sources.

我正在研究物流系统中的 AI/ML 应用。背景：我们公司正在考虑集成预测路线规划。输出：关于当前趋势、供应商和实施模式的五段式总结。包含引用和链接。

I’m researching AI/ML adoption in logistics systems. Context: Our company is considering integrating predictive routing. Output: A 5-paragraph summary on current trends, vendors, and implementation patterns. Include citations and links.

研究 GDPR/CCPA 合规的最佳实践，以便我们可以帮助启动与法务团队的讨论。背景：我们的应用在欧盟和美国存储敏感用户数据。输出：按法规排序的合规清单，包含引用。
包含文档和法规的链接。

Research best practices for GDPR/CCPA compliance so we can help kick off discussions with our legal team. Context: Our app stores sensitive user data in the EU and US. Output: A compliance checklist with citations, sorted by regulation.
Include links to documentation and regulations.

我已为[插入项目或功能]草拟了一份技术设计文档。请检查其清晰度、架构合理性和完整性。突出任何遗漏的考虑因素或审阅者可能提出的问题。

I’ve drafted a technical design document for [insert project or feature]. Review it for clarity, architectural soundness, and completeness. Highlight any missing considerations or questions reviewers may raise.

我需要为其他开发者记录这个内部 API 的工作原理。以下是相关的代码、架构和示例用法：[插入材料]。创建清晰的文档，包括端点、输入/输出格式和预期行为。

I need to document how this internal API works for other developers. Here’s the relevant code, schema, and usage examples: [insert materials]. Create clear documentation including endpoints, input/output formats, and expected behavior.

我需要为支持[插入系统]的值班工程师创建一个运行手册。草拟一个包含系统概述、常见警报、诊断步骤和升级流程的章节。

I need to create a runbook for on-call engineers supporting [insert system]. Draft one that includes sections for system overview, common alerts, diagnostic steps, and escalation procedures.

我需要为加入[插入团队]的新工程师编写一个入职指南。创建一个包含所需工具、访问设置、代码库概述和首次任务的草稿，使其适合自助式入职。

I need to write an onboarding guide for new engineers joining [insert team]. Create a draft with sections for required tools, access setup, codebase overview, and first tasks. Make it suitable for self-service onboarding.

根据这个工程规范[插入任务或功能]，编写一个 JIRA 工单，包括问题描述、背景、目标、验收标准和实施技术说明。

Based on this engineering spec for [insert task or feature], write a JIRA ticket that includes the problem statement, context, goals, acceptance criteria, and technical notes for implementation.

一个生产系统间歇性出现故障，我们正努力找出根本原因。根据以下日志、指标和最近的变化：[插入上下文]，帮助识别最可能的原因并建议缓解的下一步措施。

A system in production is intermittently failing, and we’re struggling to isolate the root cause. Based on the following logs, metrics, and recent changes: [insert context], help identify the most likely causes and suggest next steps for mitigation.

我们的服务在高峰使用期间出现延迟和性能下降。以下是指标、日志和相关跟踪信息：[插入上下文]。帮助识别瓶颈并推荐具体的优化措施。

Our service is experiencing latency and degraded performance during peak usage. Here are metrics, logs, and relevant traces: [insert context]. Help identify the bottlenecks and recommend specific optimizations.

昨天的运行中，一个关键的数据管道失败了。以下是日志、数据量趋势和错误输出：[插入上下文]。分析可能出错的原因，并提供防止再次发生的建议。

A critical data pipeline failed in yesterday’s run. Here are the logs, data volume trends, and error outputs: [insert context]. Analyze what likely went wrong and provide recommendations to prevent recurrence.

我们目前使用[插入工具]来监控[插入服务]。审查我们的可观测性设置，并在指标、日志记录、警报和仪表板方面提出改进建议，以提高问题检测和调试能力。

We currently use [insert tools] for monitoring [insert service]. Review our observability setup and suggest improvements across metrics, logging, alerting, and dashboards to improve issue detection and debugging.

我们正在为[插入功能/系统]准备测试用例。头脑风暴可能未被标准测试覆盖的潜在边缘情况和故障场景，包括非正常用户输入、系统状态变化和并发问题。

We’re preparing test cases for [insert feature/system]. Brainstorm potential edge cases and failure scenarios that may not be covered by standard testing, including unusual user inputs, system state changes, and concurrency issues.

分析这个产品使用日志的 CSV 文件。背景：我们希望识别随时间推移和使用者细分的市场趋势。输出：汇总统计数据+突出关键趋势的折线图或柱状图。

Analyze this CSV of product usage logs. Context: We want to identify usage trends over time and across user segments. Output: Summary stats + line or bar charts highlighting key trends.

从该数据集绘制随时间变化的错误率。背景：它包含过去一个月的应用日志。输出：一个带有错误峰值标注的时间序列图表和简短的解释。

Plot error rates over time from this dataset. Context: It contains application logs from the last month. Output: A time-series chart with callouts for error spikes and a short interpretation.

分析这组性能测试结果。背景：它比较了我们后端服务的两个版本。输出：并排比较图表 + 改进或退化的文本总结。

Analyze this set of performance test results. Context: It compares two versions of our backend service. Output: Side-by-side comparison charts + text summary of improvements or regressions.

分析这个错误报告数据集。背景：每一行包含严重程度、频率和受影响的用户。输出：一个按优先级排列的顶级错误列表，以及显示频率与严重程度对比的图表。

Analyze this bug report dataset. Context: Each row includes severity, frequency, and affected users. Output: A prioritized list of top bugs with charts showing frequency vs. severity.

总结这个用户反馈 CSV 文件。背景：它包含最近一次调查的评分和开放式文本回复。输出：关键主题、情感分数，以及显示评分分布的图表。

Summarize this user feedback CSV. Context: It includes ratings and open text responses from a recent survey. Output: Key themes, sentiment scores, and charts showing distribution of ratings.

我需要可视化[插入系统或服务]的架构。生成一个组件图，展示关键服务、数据流和第三方集成。使用清晰的标签并逻辑分组组件。

I need to visualize the architecture of [insert system or service]. Generate a component diagram showing key services, data flows, and third-party integrations. Use clear labels and group components logically.

创建系统架构图。背景：这是一个基于微服务的电子商务平台，包含支付、目录和用户资料等服务。输出：带有标签服务和数据流箭头的图表。

Create an image of the system architecture. Context: It’s a microservices-based e-commerce platform with services for payments, catalog, and user profiles. Output: Diagram with labeled services and data flow arrows.

创建一个解释我们 CI/CD 流程的图片。背景：这是用于向商业利益相关者做的演示。输出：显示开发→构建→测试→部署步骤的图表，配有基本图标和简短描述。

Create an image that explains our CI/CD process. Context: This is for a presentation to business stakeholders. Output: Diagram showing dev → build → test → deploy steps with basic icons and short descriptions.

创建一个展示机器学习管道中数据流的图像。背景：我们收集原始用户数据，进行清洗，训练模型，并提供预测。输出：从原始数据到推理的带标签流程图。

Create an image showing data flow in a machine learning pipeline. Context: We collect raw user data, clean it, train models, and serve predictions. Output: A labeled flowchart from raw data to inference.

创建我们移动银行应用程序的客户旅程地图。背景：步骤包括入职、账户链接、交易和支持。输出：带有步骤、屏幕和决策点的可视化流程图。

Create a customer journey map through our mobile banking app. Context: Steps include onboarding, account linking, transactions, and support. Output: A visual flowchart with steps, screens, and decision points.