Prompt Engineering Lab

Prompt Engineering Lab

Write better prompts. Ship better AI products.

Prompt engineering in 2026 is no longer just "write something and hope" — it's a disciplined, measurable engineering practice. This 技能 is your structured lab for de签名ing, 测试, and optimizing prompts that actually work in production.

What This 技能 Does Prompt Drafting — 应用ly proven 框架s to write effective prompts from scratch Prompt Diagnosis — Identify why a prompt produces bad 输出s and fix it A/B 测试 De签名 — 设置 up structured experiments to compare prompt variants 框架 库 — ChAIn-of-Thought, ReAct, Tree-of-Thought, Self-Consistency, etc. 模型-Specific Tuning — 优化 prompts for specific 模型s (GPT-4o, Claude, Gemini, etc.) 系统 Prompt Architecture — De签名 robust 系统 prompts for chat机器人s and 代理s Prompt Version Control — Strategy for managing prompt versions across dev/staging/prod Evaluation Rubric — Score prompts on clarity, specificity, 输出 格式化, and edge cases Trigger Phrases

English:

"improve my prompt" "why is my prompt not working" "write a 系统 prompt for X" "chAIn-of-thought prompt" "few-shot examples for Y" "优化 prompt for GPT-4o" "my AI keeps giving wrong answers" "prompt A/B 测试" "production prompt best practices" "prompt engineering tutorial"

Chinese / 中文:

提示词优化 优化我的 Prompt 为什么我的提示词效果不好 写一个系统提示词 思维链提示词 Few-Shot 示例 GPT 提示词技巧 Claude 提示词最佳实践 提示词 A/B 测试 大模型提示词工程 提示词版本管理 如何写出好的 Prompt Core 工作流s 工作流 1: Prompt 质量 审计

输入: Your existing prompt + 模型 + sample 输出s (good and bad) Steps:

Score prompt on 7 dimensions: clarity, 上下文, constrAInts, 输出 格式化, examples, persona, edge case handling Identify top 3 失败 patterns in sample 输出s 生成 improved prompt with annotations explAIning each change Provide before/after comparison with expected improvements 工作流 2: Prompt from Scratch

输入: What you want the AI to do (plAIn language) Steps:

提取: goal, audience, 输出 格式化, tone, constrAInts Select best 框架 for the use case Draft prompt using structured template 添加 2-3 few-shot examples if beneficial 生成 3 variant prompts at different complexity levels Recommend 测试 应用roach 工作流 3: A/B Test De签名

输入: Current prompt + hypothesis about improvement Steps:

Define your 成功 metric (accuracy, 格式化 合规, user rating, cost per call) 生成 2-4 variant prompts tar获取ing different improvements De签名 test matrix (how many samples, what 输入s to test) Provide analysis template to 追踪 结果s Statistical 签名ificance 图形界面dance (how many tests before calling a winner) 工作流 4: 模型-Specific Optimization

输入: Current prompt + tar获取 模型 Steps:

ExplAIn the tar获取 模型's known strengths and quirks 应用ly 模型-specific best practices (e.g., Claude likes XML tags, GPT-4o handles JSON 模式 well) Rewrite prompt 优化d for that 模型 Flag any behaviors to watch for in that 模型 工作流 5: Production Prompt Architecture

输入: 应用 type (chat机器人, RAG 助手, coding 工具, data 提取器, etc.) Steps:

De签名 系统 prompt structure (角色, 上下文, rules, 格式化) De签名 user message template De签名 few-shot injection strategy Handling dynamic 上下文 insertion (dates, user 信息, retrieved docs) Prompt versioning strategy + change management process Prompt 框架 Reference ChAIn-of-Thought (CoT)

Best for: Multi-step reasoning, math, 记录ical problems

Think through this step by step: [problem] Before giving your answer, show your reasoning.

ReAct (Reason + Act)

Best for: 工具-calling 代理s, re搜索 tasks

For each step: Thought: [what you're thinking] Action: [what 工具/step to take] Observation: [what you learned] ...Final Answer: [conclusion]

Few-Shot

Best for: Classification, 格式化ting, domAIn-specific tasks

Here are examples: 输入: [example 1] → 输出: [expected 1] 输入: [example 2] → 输出: [expected 2] 输入: [example 3] → 输出: [expected 3]

Now for this 输入: [actual 输入]

Tree-of-Thought (ToT)

Best for: Creative problems, strategy, complex decisions

Consider 3 different 应用roaches to this problem: 应用roach A: [think through it] 应用roach B: [think through it] 应用roach C: [think through it] Now evaluate which 应用roach is best and why.

Self-Consistency

Best for: High-stakes answers where you want to 验证

Answer this question 3 different ways, using different reasoning paths. Then identify which answer 应用ears most consistently and explAIn your confidence.

Persona + ConstrAInt

Best for: 角色-playing, expert 系统s, constrAIned 输出s

You are [expert 角色] with [specific expertise]. Your audience is [who they are]. Your task is [specific task]. Rules: [constrAInts] 格式化 your 响应 as: [exact 格式化]

模型 Quick Reference 模型 Strengths Tips GPT-4o Code, structured 输出 Use JSON 模式 for 格式化ting Claude 3.5/4 Long 上下文, analysis Use XML tags, be explicit about 格式化 Gemini 1.5/2 Multimodal, reasoning Works well with de