[{"data":1,"prerenderedAt":815},["ShallowReactive",2],{"header-counts":3,"footer-counts":6,"wiki-prompt-engineering":9},{"tools":4,"reviews":5},65,7,{"tools":4,"reviews":5,"playbooks":7,"news":8},10,8,{"id":10,"title":11,"body":12,"category":795,"description":53,"extension":796,"meta":797,"navigation":218,"path":798,"published":799,"relatedModels":800,"relatedTools":803,"seo":806,"slug":807,"stem":808,"summary":809,"tags":810,"updated":799,"__hash__":814},"wiki\u002Fwiki\u002Fprompt-engineering.md","Prompt Engineering（提示词工程）",{"type":13,"value":14,"toc":757},"minimark",[15,20,24,32,35,40,43,54,58,61,67,71,74,80,83,87,90,96,109,113,116,122,126,129,135,139,142,197,260,263,299,306,310,313,477,480,498,502,509,515,518,521,532,535,539,542,546,554,558,561,565,568,574,578,581,585,595,599,602,608,611,615,618,622,629,635,639,643,646,672,676,682,685,689,692,696,699,703,706,710,713,717,720,723,753],[16,17,19],"h2",{"id":18},"什么是-prompt-engineering","什么是 Prompt Engineering",[21,22,23],"p",{},"Prompt Engineering 是设计和优化 LLM 输入提示词的技术，目标是让模型生成更准确、更有用的输出。",[21,25,26,27,31],{},"它不是\"哄模型\"，而是",[28,29,30],"strong",{},"用结构化的方式精确传达你的意图","。",[16,33,34],{"id":34},"核心技巧",[36,37,39],"h3",{"id":38},"_1-角色设定","1. 角色设定",[21,41,42],{},"给模型一个明确的角色，让它知道用什么视角回答。",[44,45,50],"pre",{"className":46,"code":48,"language":49},[47],"language-text","你是一个资深 Python 后端工程师，擅长 FastAPI 和 PostgreSQL。\n请审查以下代码的安全性和性能问题。\n","text",[51,52,48],"code",{"__ignoreMap":53},"",[36,55,57],{"id":56},"_2-few-shot-示例","2. Few-shot 示例",[21,59,60],{},"给模型几个输入输出范例，让它学会你期望的模式。",[44,62,65],{"className":63,"code":64,"language":49},[47],"输入：这个函数太慢了\n输出：该函数时间复杂度为 O(n²)，建议改为哈希表查找，降至 O(n)。\n\n输入：这里会内存泄漏\n输出：该代码创建了事件监听器但未在组件销毁时移除，导致内存无法回收。\n\n输入：{{用户输入}}\n输出：\n",[51,66,64],{"__ignoreMap":53},[36,68,70],{"id":69},"_3-chain-of-thought-cot","3. Chain-of-Thought (CoT)",[21,72,73],{},"让模型\"想一想再回答\"，显著提高推理准确率。",[44,75,78],{"className":76,"code":77,"language":49},[47],"请逐步分析以下问题，先写出推理过程，再给出最终答案。\n\n问题：一个水池有两个进水管，A 管 3 小时注满，B 管 5 小时注满，同时开几小时注满？\n",[51,79,77],{"__ignoreMap":53},[21,81,82],{},"对于 Claude Sonnet 4 \u002F GPT-5 等推理模型，不需要显式要求 CoT——它们内置了思维链能力。",[36,84,86],{"id":85},"_4-结构化输出","4. 结构化输出",[21,88,89],{},"要求模型用特定格式输出，方便程序解析。",[44,91,94],{"className":92,"code":93,"language":49},[47],"请以 JSON 格式输出，包含以下字段：\n{\n  \"bug_found\": true\u002Ffalse,\n  \"severity\": \"high\u002Fmedium\u002Flow\",\n  \"description\": \"问题描述\",\n  \"fix_suggestion\": \"修复建议\"\n}\n",[51,95,93],{"__ignoreMap":53},[21,97,98,99,104,105,108],{},"生产场景下不要靠 prompt\"求\"模型出 JSON——用 ",[100,101,103],"a",{"href":102},"\u002Fwiki\u002Ffunction-calling.html","Structured Outputs","（OpenAI）、Tool Use（Anthropic）或 ",[51,106,107],{},"responseSchema","（Gemini）做硬约束。",[36,110,112],{"id":111},"_5-约束条件","5. 约束条件",[21,114,115],{},"明确告诉模型什么该做、什么不该做。",[44,117,120],{"className":118,"code":119,"language":49},[47],"规则：\n1. 只审查安全相关的问题，不评论代码风格\n2. 如果没有安全问题，明确说\"未发现安全问题\"\n3. 每个问题必须给出具体的代码行号\n4. 不要给出模糊的建议如\"注意安全\"\n",[51,121,119],{"__ignoreMap":53},[36,123,125],{"id":124},"_6-分步指令","6. 分步指令",[21,127,128],{},"复杂任务拆成明确步骤。",[44,130,133],{"className":131,"code":132,"language":49},[47],"请按以下步骤执行：\n1. 读取 src\u002Fauth.ts 文件\n2. 找出所有 SQL 查询\n3. 检查是否使用了参数化查询\n4. 列出有 SQL 注入风险的查询\n5. 给出修复建议\n",[51,134,132],{"__ignoreMap":53},[16,136,138],{"id":137},"xml-vs-markdown-结构化","XML vs Markdown 结构化",[21,140,141],{},"主流两种 prompt 结构化方式，按模型挑：",[44,143,147],{"className":144,"code":145,"language":146,"meta":53,"style":53},"language-xml shiki shiki-themes github-light github-dark","\u003C!-- XML 风格（Anthropic 官方推荐）-->\n\u003Ccontext>\n  \u003Cfile path=\"src\u002Fauth.ts\">...\u003C\u002Ffile>\n\u003C\u002Fcontext>\n\u003Ctask>Review for security issues.\u003C\u002Ftask>\n\u003Crules>\n  \u003Crule>Only report security findings.\u003C\u002Frule>\n\u003C\u002Frules>\n","xml",[51,148,149,157,163,169,175,181,187,192],{"__ignoreMap":53},[150,151,154],"span",{"class":152,"line":153},"line",1,[150,155,156],{},"\u003C!-- XML 风格（Anthropic 官方推荐）-->\n",[150,158,160],{"class":152,"line":159},2,[150,161,162],{},"\u003Ccontext>\n",[150,164,166],{"class":152,"line":165},3,[150,167,168],{},"  \u003Cfile path=\"src\u002Fauth.ts\">...\u003C\u002Ffile>\n",[150,170,172],{"class":152,"line":171},4,[150,173,174],{},"\u003C\u002Fcontext>\n",[150,176,178],{"class":152,"line":177},5,[150,179,180],{},"\u003Ctask>Review for security issues.\u003C\u002Ftask>\n",[150,182,184],{"class":152,"line":183},6,[150,185,186],{},"\u003Crules>\n",[150,188,189],{"class":152,"line":5},[150,190,191],{},"  \u003Crule>Only report security findings.\u003C\u002Frule>\n",[150,193,194],{"class":152,"line":8},[150,195,196],{},"\u003C\u002Frules>\n",[44,198,202],{"className":199,"code":200,"language":201,"meta":53,"style":53},"language-markdown shiki shiki-themes github-light github-dark","\u003C!-- Markdown 风格（OpenAI 偏好）-->\n## Context\n\nFile: src\u002Fauth.ts\n[content]\n\n## Task\nReview for security issues.\n\n## Rules\n- Only report security findings.\n","markdown",[51,203,204,209,214,220,225,230,234,239,244,249,254],{"__ignoreMap":53},[150,205,206],{"class":152,"line":153},[150,207,208],{},"\u003C!-- Markdown 风格（OpenAI 偏好）-->\n",[150,210,211],{"class":152,"line":159},[150,212,213],{},"## Context\n",[150,215,216],{"class":152,"line":165},[150,217,219],{"emptyLinePlaceholder":218},true,"\n",[150,221,222],{"class":152,"line":171},[150,223,224],{},"File: src\u002Fauth.ts\n",[150,226,227],{"class":152,"line":177},[150,228,229],{},"[content]\n",[150,231,232],{"class":152,"line":183},[150,233,219],{"emptyLinePlaceholder":218},[150,235,236],{"class":152,"line":5},[150,237,238],{},"## Task\n",[150,240,241],{"class":152,"line":8},[150,242,243],{},"Review for security issues.\n",[150,245,247],{"class":152,"line":246},9,[150,248,219],{"emptyLinePlaceholder":218},[150,250,251],{"class":152,"line":7},[150,252,253],{},"## Rules\n",[150,255,257],{"class":152,"line":256},11,[150,258,259],{},"- Only report security findings.\n",[21,261,262],{},"经验：",[264,265,266,273,287,293],"ul",{},[267,268,269,272],"li",{},[28,270,271],{},"Claude 系列","：XML 标签明显更稳定，错读概率低",[267,274,275,278,279,282,283,286],{},[28,276,277],{},"GPT 系列","：Markdown 标题更自然，对 ",[51,280,281],{},"##"," \u002F ",[51,284,285],{},"###"," 层级敏感",[267,288,289,292],{},[28,290,291],{},"Gemini","：两者差不多",[267,294,295,298],{},[28,296,297],{},"国产模型","：建议 Markdown，部分模型对 XML 的训练数据少",[21,300,301,302,305],{},"混搭也行，但",[28,303,304],{},"一个 prompt 内挑一种贯彻到底","，不要 XML 和 Markdown 交替。",[16,307,309],{"id":308},"anthropic-openai-官方模板风格","Anthropic \u002F OpenAI 官方模板风格",[21,311,312],{},"工业级 prompt 通常长这样（Anthropic 风格示例）：",[44,314,316],{"className":144,"code":315,"language":146,"meta":53,"style":53},"\u003Crole>\nYou are a senior security engineer reviewing pull requests.\n\u003C\u002Frole>\n\n\u003Cinstructions>\n1. Read the diff in \u003Cdiff> below\n2. Identify security vulnerabilities\n3. Output findings in the format specified in \u003Coutput_format>\n\u003C\u002Finstructions>\n\n\u003Coutput_format>\nFor each finding:\n- severity: critical | high | medium | low\n- file: \u003Cpath>\n- line: \u003Cnumber>\n- issue: \u003Cone sentence>\n- fix: \u003Ccode or text>\n\u003C\u002Foutput_format>\n\n\u003Cexamples>\n\u003Cexample>\nInput: ... diff with SQL injection ...\nOutput: { \"severity\": \"critical\", \"file\": \"auth.py\", ... }\n\u003C\u002Fexample>\n\u003C\u002Fexamples>\n\n\u003Cdiff>\n{{actual diff}}\n\u003C\u002Fdiff>\n",[51,317,318,323,328,333,337,342,347,352,357,362,366,371,377,383,389,395,401,407,413,418,424,430,436,442,448,454,459,465,471],{"__ignoreMap":53},[150,319,320],{"class":152,"line":153},[150,321,322],{},"\u003Crole>\n",[150,324,325],{"class":152,"line":159},[150,326,327],{},"You are a senior security engineer reviewing pull requests.\n",[150,329,330],{"class":152,"line":165},[150,331,332],{},"\u003C\u002Frole>\n",[150,334,335],{"class":152,"line":171},[150,336,219],{"emptyLinePlaceholder":218},[150,338,339],{"class":152,"line":177},[150,340,341],{},"\u003Cinstructions>\n",[150,343,344],{"class":152,"line":183},[150,345,346],{},"1. Read the diff in \u003Cdiff> below\n",[150,348,349],{"class":152,"line":5},[150,350,351],{},"2. Identify security vulnerabilities\n",[150,353,354],{"class":152,"line":8},[150,355,356],{},"3. Output findings in the format specified in \u003Coutput_format>\n",[150,358,359],{"class":152,"line":246},[150,360,361],{},"\u003C\u002Finstructions>\n",[150,363,364],{"class":152,"line":7},[150,365,219],{"emptyLinePlaceholder":218},[150,367,368],{"class":152,"line":256},[150,369,370],{},"\u003Coutput_format>\n",[150,372,374],{"class":152,"line":373},12,[150,375,376],{},"For each finding:\n",[150,378,380],{"class":152,"line":379},13,[150,381,382],{},"- severity: critical | high | medium | low\n",[150,384,386],{"class":152,"line":385},14,[150,387,388],{},"- file: \u003Cpath>\n",[150,390,392],{"class":152,"line":391},15,[150,393,394],{},"- line: \u003Cnumber>\n",[150,396,398],{"class":152,"line":397},16,[150,399,400],{},"- issue: \u003Cone sentence>\n",[150,402,404],{"class":152,"line":403},17,[150,405,406],{},"- fix: \u003Ccode or text>\n",[150,408,410],{"class":152,"line":409},18,[150,411,412],{},"\u003C\u002Foutput_format>\n",[150,414,416],{"class":152,"line":415},19,[150,417,219],{"emptyLinePlaceholder":218},[150,419,421],{"class":152,"line":420},20,[150,422,423],{},"\u003Cexamples>\n",[150,425,427],{"class":152,"line":426},21,[150,428,429],{},"\u003Cexample>\n",[150,431,433],{"class":152,"line":432},22,[150,434,435],{},"Input: ... diff with SQL injection ...\n",[150,437,439],{"class":152,"line":438},23,[150,440,441],{},"Output: { \"severity\": \"critical\", \"file\": \"auth.py\", ... }\n",[150,443,445],{"class":152,"line":444},24,[150,446,447],{},"\u003C\u002Fexample>\n",[150,449,451],{"class":152,"line":450},25,[150,452,453],{},"\u003C\u002Fexamples>\n",[150,455,457],{"class":152,"line":456},26,[150,458,219],{"emptyLinePlaceholder":218},[150,460,462],{"class":152,"line":461},27,[150,463,464],{},"\u003Cdiff>\n",[150,466,468],{"class":152,"line":467},28,[150,469,470],{},"{{actual diff}}\n",[150,472,474],{"class":152,"line":473},29,[150,475,476],{},"\u003C\u002Fdiff>\n",[21,478,479],{},"要点：",[264,481,482,488,495],{},[267,483,484,485],{},"角色、指令、输出格式、示例、动态内容",[28,486,487],{},"各占独立标签",[267,489,490,491,494],{},"动态内容（用户输入、检索结果）",[28,492,493],{},"放最后","——既配合 prompt cache，又减少注入风险",[267,496,497],{},"Few-shot 数量 1-5 个最佳，过多反而稀释指令权重",[16,499,501],{"id":500},"推理模型的-prompt-写法不一样","推理模型的 prompt 写法不一样",[21,503,504,505,508],{},"GPT-5、Claude Opus 4 thinking、DeepSeek-R1 等推理模型",[28,506,507],{},"不应再写 CoT 指令","：",[44,510,513],{"className":511,"code":512,"language":49},[47],"❌ 老写法（对 GPT-4o 有效）\n\"请一步一步思考，先写出推理过程...\"\n\n✅ 推理模型新写法\n直接说目标和约束，不要教它怎么想\n",[51,514,512],{"__ignoreMap":53},[21,516,517],{},"原因：推理模型内置了思维链，再加 CoT 反而会让它在\"展示思考\"上花更多 token、却没有更聪明。给清楚目标和验收标准即可。",[21,519,520],{},"类似的：",[264,522,523,526,529],{},[267,524,525],{},"推理模型不需要 \"take a deep breath\" \u002F \"you are an expert\" 这类老套激励",[267,527,528],{},"Few-shot 仍然有效，但作用从\"示范怎么想\"变成\"约束输出格式\"",[267,530,531],{},"温度建议保持默认（通常 1.0），别强行调 0",[16,533,534],{"id":534},"高级技巧",[36,536,538],{"id":537},"self-consistency","Self-Consistency",[21,540,541],{},"让模型多次回答同一个问题，取多数结果。适用于数学\u002F推理题。",[36,543,545],{"id":544},"react","ReAct",[21,547,548,549,553],{},"Thought → Action → Observation 循环，",[100,550,552],{"href":551},"\u002Fwiki\u002Fai-agent.html","Agent"," 的基础模式。",[36,555,557],{"id":556},"tree-of-thoughts","Tree of Thoughts",[21,559,560],{},"让模型探索多条推理路径，选最优的。适合复杂决策。",[36,562,564],{"id":563},"prompt-chaining","Prompt Chaining",[21,566,567],{},"把一个复杂任务拆成多个 prompt 串联：",[44,569,572],{"className":570,"code":571,"language":49},[47],"Prompt 1: 提取文章关键信息 → 输出 JSON\nPrompt 2: 基于 JSON 生成摘要 → 输出摘要\nPrompt 3: 基于摘要生成社交媒体文案 → 输出文案\n",[51,573,571],{"__ignoreMap":53},[16,575,577],{"id":576},"prompt-的工程化当-prompt-变成代码","Prompt 的工程化：当 prompt 变成代码",[21,579,580],{},"生产级 prompt 不是字符串拼接，是要进 git 的\"代码\"。需要：",[36,582,584],{"id":583},"_1-版本化-code-review","1. 版本化 + Code Review",[21,586,587,588,282,591,594],{},"每个 prompt 一个文件（",[51,589,590],{},".md",[51,592,593],{},".txt","），改动走 PR review。模板插值用 Jinja2 \u002F Handlebars 而不是 f-string 散落各处。",[36,596,598],{"id":597},"_2-评测集eval-set","2. 评测集（Eval Set）",[21,600,601],{},"任何 prompt 改动前必跑回归测试：",[44,603,606],{"className":604,"code":605,"language":49},[47],"golden_dataset.jsonl\n├─ 100 条典型 case\n│  - input\n│  - expected_output（或 expected_format）\n│  - rubric（评分细则）\n└─ 跑新 prompt → 自动比对 → 通过率不降才合并\n",[51,607,605],{"__ignoreMap":53},[21,609,610],{},"工具：Promptfoo \u002F LangSmith \u002F Braintrust \u002F 自己写。",[36,612,614],{"id":613},"_3-ab-测试","3. A\u002FB 测试",[21,616,617],{},"灰度发布 prompt 变更，看真实用户场景下指标（任务成功率、用户满意度、token 成本）。",[36,619,621],{"id":620},"_4-监控","4. 监控",[21,623,624,625,628],{},"线上每个调用都记 ",[51,626,627],{},"prompt_version + input + output + tokens","，方便定位回归。",[630,631,632],"blockquote",{},[21,633,634],{},"把 prompt 当代码维护后，\"改一个字模型就崩\"的痛苦会大幅减少。这是从 demo 到生产最关键的工程化跃迁。",[16,636,638],{"id":637},"在-ai-编程中的实践","在 AI 编程中的实践",[36,640,642],{"id":641},"cursor-claude-code","Cursor \u002F Claude Code",[21,644,645],{},"这些工具内部用了大量 prompt engineering：",[264,647,648,654,660,666],{},[267,649,650,653],{},[28,651,652],{},"System Prompt"," — 定义 AI 的角色和行为规范",[267,655,656,659],{},[28,657,658],{},"Context Assembly"," — 组装文件、光标位置、对话历史",[267,661,662,665],{},[28,663,664],{},"Tool Definitions"," — 定义搜索、编辑、终端等工具",[267,667,668,671],{},[28,669,670],{},"Few-shot"," — 展示工具调用的正确格式",[36,673,675],{"id":674},"代码审查-prompt","代码审查 Prompt",[44,677,680],{"className":678,"code":679,"language":49},[47],"你是一个代码审查专家。请审查以下 Git diff：\n\n{{diff}}\n\n检查以下方面：\n1. 安全漏洞（SQL 注入、XSS、敏感信息泄露）\n2. 性能问题（N+1 查询、内存泄漏、不必要的计算）\n3. 逻辑错误（边界条件、空指针、竞态条件）\n4. 可维护性（命名、复杂度、重复代码）\n\n输出格式：\n- 🔴 严重：[问题描述] (行号)\n- 🟡 建议：[问题描述] (行号)\n- 🟢 良好：[做得好的地方]\n",[51,681,679],{"__ignoreMap":53},[16,683,684],{"id":684},"常见误区",[36,686,688],{"id":687},"_1-prompt-越长越好","1. \"Prompt 越长越好\"",[21,690,691],{},"不。无关内容会分散模型注意力。每个词都应有存在理由。",[36,693,695],{"id":694},"_2-加你是专家就有用","2. \"加'你是专家'就有用\"",[21,697,698],{},"角色设定只在角色与任务相关时有效。\"你是诗人\"对代码审查没有帮助。",[36,700,702],{"id":701},"_3-示例越多越好","3. \"示例越多越好\"",[21,704,705],{},"3-5 个精选示例通常优于 10 个冗余示例。质量 > 数量。",[36,707,709],{"id":708},"_4-一个-prompt-解决所有问题","4. \"一个 prompt 解决所有问题\"",[21,711,712],{},"复杂任务应该用 prompt chaining 或 agent 模式拆分，而不是写一个巨型 prompt。",[36,714,716],{"id":715},"_5-调好的-prompt-一直能用","5. \"调好的 prompt 一直能用\"",[21,718,719],{},"模型每次升级（GPT-4 → GPT-5），prompt 行为都会微变。改基础模型必须重跑评测集。",[16,721,722],{"id":722},"延伸阅读",[264,724,725,733,740,746],{},[267,726,727,728,732],{},"进阶视角：",[100,729,731],{"href":730},"\u002Fwiki\u002Fcontext-engineering.html","Context Engineering","——Prompt 是其中一部分",[267,734,735,736,739],{},"结构化输出：",[100,737,738],{"href":102},"Function Calling"," 的 Structured Outputs",[267,741,742,743],{},"Agent 中的 prompt：",[100,744,745],{"href":551},"AI Agent",[267,747,748,749],{},"控制随机性：",[100,750,752],{"href":751},"\u002Fwiki\u002Ftemperature-top-p.html","Temperature 与 Top-P",[754,755,756],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":53,"searchDepth":165,"depth":165,"links":758},[759,760,768,769,770,771,777,783,787,794],{"id":18,"depth":159,"text":19},{"id":34,"depth":159,"text":34,"children":761},[762,763,764,765,766,767],{"id":38,"depth":165,"text":39},{"id":56,"depth":165,"text":57},{"id":69,"depth":165,"text":70},{"id":85,"depth":165,"text":86},{"id":111,"depth":165,"text":112},{"id":124,"depth":165,"text":125},{"id":137,"depth":159,"text":138},{"id":308,"depth":159,"text":309},{"id":500,"depth":159,"text":501},{"id":534,"depth":159,"text":534,"children":772},[773,774,775,776],{"id":537,"depth":165,"text":538},{"id":544,"depth":165,"text":545},{"id":556,"depth":165,"text":557},{"id":563,"depth":165,"text":564},{"id":576,"depth":159,"text":577,"children":778},[779,780,781,782],{"id":583,"depth":165,"text":584},{"id":597,"depth":165,"text":598},{"id":613,"depth":165,"text":614},{"id":620,"depth":165,"text":621},{"id":637,"depth":159,"text":638,"children":784},[785,786],{"id":641,"depth":165,"text":642},{"id":674,"depth":165,"text":675},{"id":684,"depth":159,"text":684,"children":788},[789,790,791,792,793],{"id":687,"depth":165,"text":688},{"id":694,"depth":165,"text":695},{"id":701,"depth":165,"text":702},{"id":708,"depth":165,"text":709},{"id":715,"depth":165,"text":716},{"id":722,"depth":159,"text":722},"methodology","md",{},"\u002Fwiki\u002Fprompt-engineering","2026-06-21",[801,802],"claude-sonnet-4","gpt-5",[804,805],"coding\u002Fide\u002Fcursor","coding\u002Fcli\u002Fclaude-code",{"title":11,"description":53},"prompt-engineering","wiki\u002Fprompt-engineering","通过精心设计提示词来引导 LLM 生成高质量输出的技术，包括 few-shot、CoT、角色设定、结构化输出等方法。",[811,812,813,670],"Prompt","提示词","CoT","WvZdrEDRd2GefeD5a7RR5CZpK25BZFdxSt6HqSH4IQo",1782316490759]