Translation Comparison - amplify

🇺🇸

Original

English

🇨🇳

Translation

Chinese

MANDATORY PREPARATION

强制准备

Invoke {{command_prefix}}agent-workflow — it contains workflow principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run {{command_prefix}}teach-maestro first. Consult the tool-orchestration reference in the agent-workflow skill for adding tools effectively.

Take a working workflow and make it more capable. Amplification adds new abilities without breaking existing functionality.

调用 {{command_prefix}}agent-workflow —— 它包含了工作流原则、反模式，以及上下文收集协议。在继续操作前请遵循该协议——如果尚未存在工作流上下文，你必须首先运行 {{command_prefix}}teach-maestro。参考agent-workflow技能中的工具编排指南来高效添加工具。

基于可正常运行的工作流进一步提升其能力。能力增强会在不破坏现有功能的前提下新增能力。

Amplification Strategies

增强策略

Better Prompts

Add few-shot examples for edge cases the model currently mishandles
Add chain-of-thought for tasks where reasoning quality matters
Add negative instructions for common mistakes
Upgrade output schema with more structured fields

Better Tools

Add tools for capabilities the model currently lacks
Improve existing tool descriptions for better selection accuracy
Add confirmation steps for high-stakes operations
Add tools for verification/validation of outputs

Better Context

Add RAG for domain-specific knowledge
Add real-time data sources for current information
Add user profile/history for personalization
Add project documentation as reference context

Better Models

Upgrade to a more capable model for critical steps
Use model cascading (cheap model for simple, capable model for complex)
Add vision capabilities if processing images/documents
Add code execution capabilities if generating code

更优提示词

针对模型当前处理不当的边缘场景添加小样本示例
对推理质量要求较高的任务添加思维链引导
针对常见错误添加否定指令
通过新增更多结构化字段升级输出schema

更优工具

新增工具来弥补模型当前缺失的能力
优化现有工具的描述以提升选择准确率
为高风险操作添加确认步骤
新增用于输出校验/验证的工具

更优上下文

为领域特定知识添加RAG
新增实时数据源来获取最新信息
新增用户画像/历史记录来实现个性化
引入项目文档作为参考上下文

更优模型

为关键步骤升级到能力更强的模型
使用模型级联（简单任务用低成本模型，复杂任务用高能力模型）
如果需要处理图像/文档则新增视觉能力
如果需要生成代码则新增代码执行能力

Amplification Process

增强流程

Identify the gap: What can't the workflow do that it should?
Choose the strategy: Which amplification approach addresses the gap?
Implement incrementally: Add one capability at a time
Verify: Run the evaluation suite to confirm improvement without regression

识别差距：工作流目前无法完成但应该具备的能力是什么？
选择策略：哪种增强方案可以解决该差距？
渐进式实现：每次仅新增一项能力
验证：运行评估套件确认能力得到提升且没有出现功能退化

Impact Assessment

影响评估

Strategy	Cost Impact	Latency Impact	Quality Impact
Better prompts	None	None	Medium-High
Better tools	Low	Low-Medium	High
Better context (RAG)	Medium	Medium	High
Better models	High	Medium-High	High

策略	成本影响	延迟影响	质量影响
更优提示词	无	无	中-高
更优工具	低	低-中	高
更优上下文（RAG）	中	中	高
更优模型	高	中-高	高

Amplification Checklist

增强检查清单

Gap identified with concrete evidence (not assumption)
Single strategy selected (don't amplify everything at once)
Baseline quality score recorded before change
Change implemented and tested
Quality score improved without regression
Cost/latency impact documented

已通过具体证据（而非假设）识别出差距
已选定单一策略（不要一次性增强所有内容）
变更前已记录基线质量评分
变更已实现并经过测试
质量评分得到提升且无功能退化
已记录成本/延迟影响

Recommended Next Step

Original

English

🇨🇳

Translation

Chinese

MANDATORY PREPARATION

强制准备

Take a working workflow and make it more capable. Amplification adds new abilities without breaking existing functionality.

基于可正常运行的工作流进一步提升其能力。能力增强会在不破坏现有功能的前提下新增能力。

Amplification Strategies

增强策略

Better Prompts

Add few-shot examples for edge cases the model currently mishandles
Add chain-of-thought for tasks where reasoning quality matters
Add negative instructions for common mistakes
Upgrade output schema with more structured fields

Better Tools

Add tools for capabilities the model currently lacks
Improve existing tool descriptions for better selection accuracy
Add confirmation steps for high-stakes operations
Add tools for verification/validation of outputs

Better Context

Add RAG for domain-specific knowledge
Add real-time data sources for current information
Add user profile/history for personalization
Add project documentation as reference context

Better Models

Upgrade to a more capable model for critical steps
Use model cascading (cheap model for simple, capable model for complex)
Add vision capabilities if processing images/documents
Add code execution capabilities if generating code

更优提示词

针对模型当前处理不当的边缘场景添加小样本示例
对推理质量要求较高的任务添加思维链引导
针对常见错误添加否定指令
通过新增更多结构化字段升级输出schema

更优工具

新增工具来弥补模型当前缺失的能力
优化现有工具的描述以提升选择准确率
为高风险操作添加确认步骤
新增用于输出校验/验证的工具

更优上下文

为领域特定知识添加RAG
新增实时数据源来获取最新信息
新增用户画像/历史记录来实现个性化
引入项目文档作为参考上下文

更优模型

为关键步骤升级到能力更强的模型
使用模型级联（简单任务用低成本模型，复杂任务用高能力模型）
如果需要处理图像/文档则新增视觉能力
如果需要生成代码则新增代码执行能力

Amplification Process

增强流程

Identify the gap: What can't the workflow do that it should?
Choose the strategy: Which amplification approach addresses the gap?
Implement incrementally: Add one capability at a time
Verify: Run the evaluation suite to confirm improvement without regression

识别差距：工作流目前无法完成但应该具备的能力是什么？
选择策略：哪种增强方案可以解决该差距？
渐进式实现：每次仅新增一项能力
验证：运行评估套件确认能力得到提升且没有出现功能退化

Impact Assessment

影响评估

Strategy	Cost Impact	Latency Impact	Quality Impact
Better prompts	None	None	Medium-High
Better tools	Low	Low-Medium	High
Better context (RAG)	Medium	Medium	High
Better models	High	Medium-High	High

策略	成本影响	延迟影响	质量影响
更优提示词	无	无	中-高
更优工具	低	低-中	高
更优上下文（RAG）	中	中	高
更优模型	高	中-高	高

Amplification Checklist

增强检查清单

Gap identified with concrete evidence (not assumption)
Single strategy selected (don't amplify everything at once)
Baseline quality score recorded before change
Change implemented and tested
Quality score improved without regression
Cost/latency impact documented

已通过具体证据（而非假设）识别出差距
已选定单一策略（不要一次性增强所有内容）
变更前已记录基线质量评分
变更已实现并经过测试
质量评分得到提升且无功能退化
已记录成本/延迟影响

amplify

Original

Translation

MANDATORY PREPARATION

强制准备

Amplification Strategies

增强策略

Amplification Process

增强流程

Impact Assessment

影响评估

Amplification Checklist

增强检查清单

Recommended Next Step

推荐后续步骤

amplify

Original

Translation

MANDATORY PREPARATION

强制准备

Amplification Strategies

增强策略

Amplification Process

增强流程

Impact Assessment

影响评估

Amplification Checklist

增强检查清单

Recommended Next Step

推荐后续步骤