To translate "Prompting techniques using instructor" into simplified Chinese while keeping the HTML structure, you would use the following: ```html 提示技术使用指导员 ``` This maintains the structure and directly translates the phrase into simplified Chinese.

To translate the English text "Let’s apply the prompting techniques mentioned in 'The Prompt Report- A Systematic Survey of Prompting Techniques' using instructor" into simplified Chinese while keeping the HTML structure, you would write: ```html 让我们使用教练员在《提示报告-提示技术的系统调查》中提到的提示技术。 ``` In this translation: - "Let’s apply" is translated as "让我们使用", which means "let's use". - "prompting techniques" is translated as "提示技术". - "mentioned in" is translated as "在...中提到的". - "using instructor" is translated as "使用教练员". Make sure the HTML tags (if any) are correctly maintained around the Chinese text as per your requirement.

在保持HTML结构的情况下，将以下英文文本翻译成简体中文： 1. 文本提示技术

To translate "In-Context Learning (ICL)" into simplified Chinese while keeping HTML structure, you can use the following: ```html 上下文学习（ICL） ``` This HTML snippet includes the translation "上下文学习（ICL）" where: - "上下文学习" translates to "In-Context Learning". - "（ICL）" remains in English letters as an acronym. Make sure your HTML document has the correct encoding (UTF-8) to display Chinese characters properly.

Sure, here's the translation in simplified Chinese while keeping the HTML structure intact: ```html

定义与实施

Certainly! Here's the HTML structure with the translated simplified Chinese text: ```html

In-Context Learning (ICL) 指的是在提示中提供示例以引导模型生成回应。这种技术对于教导模型新任务或领域特别有效。

Sure, here's the translation: 示例：少样本和零样本提示技术

这里是如何使用Instructor库实现ICL的方法：

from openai import OpenAI
import instructor
from pydantic import BaseModel

# Apply the patch to the OpenAI client
client = instructor.from_openai(OpenAI())

class ClassificationResult(BaseModel):
    label: str

def few_shot_prompting(examples: list, query: str) -> ClassificationResult:
    """
    Perform few-shot prompting using provided examples and query.
    """
    messages = [
        {"role": "system", "content": "You are an expert text classifier."},
    ]
    
    for example in examples:
        messages.append({"role": "user", "content": example["input"]})
        messages.append({"role": "assistant", "content": example["output"]})
    
    messages.append({"role": "user", "content": query})
    
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=ClassificationResult,
        messages=messages,
    )

# Example usage
examples = [
    {"input": "Classify the following text: 'I love this product!'", "output": "Positive"},
    {"input": "Classify the following text: 'This is the worst experience ever.'", "output": "Negative"},
]
query = "Classify the following text: 'I am not sure if I like it or not.'"
result = few_shot_prompting(examples, query)
print(result.label)

在保持HTML结构的情况下，将“Zero-Shot Prompting”翻译为简体中文可以是：“零-shot提示”。

在保持HTML结构的情况下，将以下英文文本翻译为简体中文： 定义和应用

在保持HTML结构的情况下，将以下英文文本翻译为简体中文： 零-shot提示涉及在没有任何示例的情况下给模型一个任务。当您希望模型能从其训练数据中进行泛化时，这是很有用的。

Sure, here's the translated text in simplified Chinese while keeping the HTML structure: ```html 角色提示及其对模型输出的影响 ```

Sure, here is the simplified Chinese translation of the text while maintaining the HTML structure: ```html 角色提示定义了助理的角色，以指导模型的响应。 ```

class ZeroShotResult(BaseModel):
    label: str

def zero_shot_prompting(query: str) -> ZeroShotResult:
    """
    Perform zero-shot prompting using provided query.
    """
    messages = [
        {"role": "system", "content": "You are a world-class text classifier."},
        {"role": "user", "content": query},
    ]
    
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=ZeroShotResult,
        messages=messages,
    )

# Example usage
query = "Classify the following text: 'This is an average product.'"
result = zero_shot_prompting(query)
print(result.label)

在保留HTML结构的前提下，将“Thought Generation”翻译为简体中文： 思维生成

在模型中激发思维过程的技巧

Certainly! Here's the translation of the English text into simplified Chinese, while keeping the HTML structure: ```html 思维生成技术可以帮助模型详细阐述想法或生成创意内容。 ``` In this HTML snippet, the Chinese text is enclosed within `` tags to indicate the language is Chinese (Simplified). This ensures proper handling of language-specific features by browsers or text processors.

Sure, here is the translated text in simplified Chinese, keeping the HTML structure intact: ```html

实际例子和使用案例

class ThoughtGenerationResult(BaseModel):
    thought: str

def generate_thoughts(prompt: str) -> ThoughtGenerationResult:
    """
    Generate thoughts based on the given prompt.
    """
    messages = [
        {"role": "system", "content": "You are a creative thinker."},
        {"role": "user", "content": prompt},
    ]
    
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=ThoughtGenerationResult,
        messages=messages,
    )

# Example usage
prompt = "What are some innovative uses of artificial intelligence in education?"
result = generate_thoughts(prompt)
print(result.thought)

To translate "Decomposition" to simplified Chinese while keeping HTML structure intact, you would use the following: ```html 分解 ``` This HTML code embeds the simplified Chinese translation "分解" within a `` tag and specifies the language using the `lang="zh"` attribute.

Sure, here's the translated text in simplified Chinese while maintaining the HTML structure: ```html Breaking Down Complex Tasks into Simpler Prompts ``` Translated to simplified Chinese: ```html 将复杂任务拆分为简单提示 ```

Sure, here's the translated text in simplified Chinese, keeping the HTML structure: ```html 分解涉及将复杂任务分解为较小、可管理的提示。 ``` This maintains the original meaning while ensuring it can be integrated into an HTML document.

Sure, here's the text translated to simplified Chinese while keeping the HTML structure: ```html

逐步指南和示例

class DecompositionResult(BaseModel):
    steps: list

def decompose_task(task: str) -> DecompositionResult:
    """
    Decompose a complex task into smaller steps.
    """
    messages = [
        {"role": "system", "content": "You are an expert at breaking down complex tasks into smaller steps."},
        {"role": "user", "content": f"Break down the following task into smaller steps: {task}"},
    ]
    
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=DecompositionResult,
        messages=messages,
    )

# Example usage
task = "Plan a project to develop a new software application."
result = decompose_task(task)
print(result.steps)

Sure, the simplified Chinese translation of "Ensembling" while keeping the HTML structure intact would be: ```html 集成中文> ```

Sure, here's the translated text in simplified Chinese while keeping the HTML structure: ```html

结合多个提示以提升性能

To translate "Ensembling involves using multiple prompts and combining their outputs to achieve better results" into simplified Chinese while keeping the HTML structure, you can use the following code snippet: ```html

Ensembling involves using multiple prompts and combining their outputs to achieve better results.

集成使用多个提示，并结合它们的输出以获得更好的结果。

Sure, the translation of "Methods and Examples" into simplified Chinese while keeping the HTML structure intact would be: ```html 方法和示例 ``` This HTML code embeds the simplified Chinese translation within a `` element and specifies the language using the `lang` attribute.

class EnsembleResult(BaseModel):
    combined_result: str

def ensemble_prompting(prompts: list) -> EnsembleResult:
    """
    Combine multiple prompts to enhance performance.
    """
    combined_output = ""
    
    for prompt in prompts:
        messages = [
            {"role": "system", "content": "You are a knowledgeable assistant."},
            {"role": "user", "content": prompt},
        ]
        result = client.chat.completions.create(
            model="gpt-3.5-turbo",
            response_model=EnsembleResult,
            messages=messages,
        )
        combined_output += result.combined_result + "\n"
    
    return EnsembleResult(combined_result=combined_output.strip())

# Example usage
prompts = [
    "Explain the concept of machine learning.",
    "Describe the benefits of using machine learning in healthcare.",
    "What are the challenges of implementing machine learning models?"
]
result = ensemble_prompting(prompts)
print(result.combined_result)

自我批评

在保持HTML结构的情况下，将“Techniques for Generating Self-Critical Responses”翻译为简体中文的文本如下： 生成自我批评性回应的技术

在保持HTML结构的情况下，将以下英文文本翻译为简体中文： 自我批评技术有助于模型生成批评其自身输出的响应。

To translate "Applications and Examples" into simplified Chinese while keeping HTML structure, you can use the following: ```html 应用和示例 ``` This HTML snippet ensures that the text "Applications and Examples" is displayed in simplified Chinese characters, while the `lang="zh-CN"` attribute specifies the language for proper language handling and accessibility.

class SelfCriticismResult(BaseModel):
    critique: str

def generate_self_criticism(prompt: str) -> SelfCriticismResult:
    """
    Generate a self-critical response based on the given prompt.
    """
    messages = [
        {"role": "system", "content": "You are a reflective thinker."},
        {"role": "user", "content": prompt},
    ]
    
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=SelfCriticismResult,
        messages=messages,
    )

# Example usage
prompt = "Review the following paragraph for any potential flaws: 'Artificial intelligence is the future of technology. It will solve all our problems.'"
result = generate_self_criticism(prompt)
print(result.critique)

To translate "2. Task-Oriented Prompting Techniques" into simplified Chinese while keeping the HTML structure, you can use the following: ```html 2. 任务导向提示技术 ``` This HTML snippet will display the translated text "任务导向提示技术" on a webpage, where "任务导向提示技术" corresponds to "Task-Oriented Prompting Techniques" in simplified Chinese.

To translate "Chain of Thought (CoT)" into simplified Chinese while keeping the HTML structure intact, you can use the following: ```html 思维链 (CoT) ``` In this HTML snippet: - `` is used to denote a section of text that needs special styling or attention. - `lang="zh-CN"` specifies the language of the enclosed text is simplified Chinese. - The text "思维链 (CoT)" inside the `` tag translates to "Chain of Thought (CoT)" in English.

在保持HTML结构的前提下，将以下英文文本翻译为简体中文： 定义与实现

Sure, here's the translated text in simplified Chinese, while keeping the HTML structure: ```html

思维链 (CoT) 提示有助于模型通过将推理过程分解为较小、逻辑步骤，为复杂问题生成逐步解决方案。

Sure, the translation of "Practical Example" into simplified Chinese while keeping HTML structure would look like this: ```html 实际示例 ``` In this example: - `` is used to apply language-specific styling or behavior. - `lang="zh-CN"` specifies the language as simplified Chinese. - `"实际示例"` is the translation of "Practical Example" into simplified Chinese characters.

from pydantic import BaseModel

class CoTResult(BaseModel):
    steps: list

def chain_of_thought(prompt: str) -> CoTResult:
    """
    Generate a step-by-step solution for the given prompt.
    """
    messages = [
        {"role": "system", "content": "You are a logical thinker."},
        {"role": "user", "content": f"Break down the following problem into a step-by-step solution: {prompt}"},
    ]
    
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=CoTResult,
        messages=messages,
    )

# Example usage
prompt = "Calculate the total cost of a shopping list with items priced at $10, $20, and $30, including a 10% tax."
result = chain_of_thought(prompt)
print(result.steps)

Sure, here is the translated text in simplified Chinese while maintaining the HTML structure: ```html 自洽性 ```

Sure, here is the translation in simplified Chinese while keeping the HTML structure: ```html

定义和实现

自我一致性涉及为相同提示生成多个答案，然后选择最一致的答案。

Sure, here's the translated text in simplified Chinese: 实际例子

from typing import List
from pydantic import BaseModel

class SelfConsistencyResult(BaseModel):
    consistent_answer: str

def self_consistency(prompt: str, n: int = 5) -> SelfConsistencyResult:
    """
    Generate multiple answers for the same prompt and choose the most consistent one.
    """
    answers = []
    for _ in range(n):
        messages = [
            {"role": "system", "content": "You are a consistent thinker."},
            {"role": "user", "content": prompt},
        ]
        result = client.chat.completions.create(
            model="gpt-3.5-turbo",
            response_model=SelfConsistencyResult,
            messages=messages,
        )
        answers.append(result.consistent_answer)
    
    # Choose the most frequent answer
    most_consistent = max(set(answers), key=answers.count)
    return SelfConsistencyResult(consistent_answer=most_consistent)

# Example usage
prompt = "What is the capital of France?"
result = self_consistency(prompt)
print(result.consistent_answer)

Sure, the translation of "Planning" into simplified Chinese while keeping HTML structure would be: ```html 规划 ``` This code ensures that "规划" (which means "Planning" in simplified Chinese) is correctly displayed and identified as Chinese text within an HTML context.

Sure, here's the translation in simplified Chinese while maintaining the HTML structure: ```html

复杂任务规划技巧

在保持HTML结构的情况下，将以下英文文本翻译为简体中文： 规划技术帮助模型将复杂任务分解为一系列动作或步骤。

Sure, here's the translation in simplified Chinese while keeping the HTML structure: ```html

实际示例

class PlanningResult(BaseModel):
    plan: list

def plan_task(prompt: str) -> PlanningResult:
    """
    Generate a plan for the given task.
    """
    messages = [
        {"role": "system", "content": "You are a strategic planner."},
        {"role": "user", "content": f"Plan the following task step by step: {prompt}"},
    ]
    
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=PlanningResult,
        messages=messages,
    )

# Example usage
prompt = "Organize a conference on artificial intelligence."
result = plan_task(prompt)
print(result.plan)

Sure, here's the translation of "Tool Use" into simplified Chinese while keeping the HTML structure: ```html 工具使用 ``` This HTML snippet maintains the structure and simply replaces the English text with its simplified Chinese equivalent.

在保留HTML结构的情况下，将以下英文文本翻译成简体中文： 整合外部工具以增强提示

工具使用涉及整合外部工具以增强模型的功能，如计算器、数据库或 API。

实际例子

class ToolUseResult(BaseModel):
    result: str

def use_tool(prompt: str, tool_function) -> ToolUseResult:
    """
    Use an external tool to enhance the model's capabilities.
    """
    tool_output = tool_function(prompt)
    messages = [
        {"role": "system", "content": "You are an assistant that uses external tools to provide accurate answers."},
        {"role": "user", "content": f"Use the following tool output to answer the question: {tool_output}"},
    ]
    
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=ToolUseResult,
        messages=messages,
    )

# Example usage with a hypothetical external tool function
def example_tool_function(prompt):
    return f"Processed result for: {prompt}"

prompt = "Calculate the distance between New York and Los Angeles."
result = use_tool(prompt, example_tool_function)
print(result.result)

Sure, the translation of "Role Play" into simplified Chinese while keeping HTML structure would look like this: ```html 角色扮演 ``` Here, `` tags are used to wrap the translated text to maintain the HTML structure.

Sure, here is the translated text in simplified Chinese while keeping the HTML structure: ```html

定义和应用

Sure, here is the translated text in simplified Chinese while maintaining the HTML structure: ```html 角色扮演涉及为模型设定一个角色，引导其回应，使其表现得像某个特定领域的专家。 ``` In HTML format: ```html 角色扮演涉及为模型设定一个角色，引导其回应，使其表现得像某个特定领域的专家。 ``` This translation keeps the original meaning while adhering to the HTML structure.

Sure, here's the translated text in simplified Chinese within the HTML structure: ```html

实际例子

class RolePlayResult(BaseModel):
    advice: str

def role_play(prompt: str, role: str) -> RolePlayResult:
    """
    Perform role play by setting a role for the model.
    """
    messages = [
        {"role": "system", "content": f"You are an expert {role}."},
        {"role": "user", "content": prompt},
    ]
    
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=RolePlayResult,
        messages=messages,
    )

# Example usage
prompt = "What are the best practices for software development?"
role = "software engineer"
result = role_play(prompt, role)
print(result.advice)

互动提示

To translate "Techniques for Interactive Dialogues" into simplified Chinese while keeping the HTML structure intact, you can use the following: ```html 交互式对话技巧 ``` This HTML snippet ensures that the text "交互式对话技巧" (which means "Techniques for Interactive Dialogues" in simplified Chinese) is properly marked as Chinese text within an HTML document.

交互提示涉及创建对话，模型和用户进行来回对话以完善响应。

Sure, here is the translation of "Practical Example" into simplified Chinese while keeping the HTML structure: ```html 实际例子 ``` In this HTML snippet: - `` is used to mark the text that needs translation. - `lang="zh"` indicates that the text inside the `` is in simplified Chinese. - `"实际例子"` is the translation of "Practical Example" into simplified Chinese.

class InteractiveResult(BaseModel):
    response: str

def interactive_prompting(initial_prompt: str, follow_up: str) -> InteractiveResult:
    """
    Create an interactive dialogue with the model.
    """
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": initial_prompt},
        {"role": "assistant", "content": follow_up},
    ]
    
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=InteractiveResult,
        messages=messages,
    )

# Example usage
initial_prompt = "Explain the concept of blockchain technology."
follow_up = "How does it differ from traditional databases?"
result = interactive_prompting(initial_prompt, follow_up)
print(result.response)

Sure, here is the translation in simplified Chinese while keeping the HTML structure: ```html 3. 高级提示技术 ``` In the HTML code above, `高级提示技术` translates to "Advanced Prompting Techniques" in simplified Chinese.

Sure, here's the translation in simplified Chinese while keeping the HTML structure: ```html

提示链接

Sure, here is the translation of "Definition and Implementation" into simplified Chinese, while keeping the HTML structure: ```html

class PromptChainingResult(BaseModel):
    final_result: str

def prompt_chaining(initial_prompt: str, chain_prompts: list) -> PromptChainingResult:
    """
    Implement prompt chaining by using the output of one prompt as the input for the next.
    """
    current_result = initial_prompt
    for prompt in chain_prompts:
        messages = [
            {"role": "system", "content": "You are an assistant that builds on previous responses."},
            {"role": "user", "content": f"{current_result} {prompt}"},
        ]
        result = client.chat.completions.create(
            model="gpt-3.5-turbo",
            response_model=PromptChainingResult,
            messages=messages,
        )
        current_result = result.final_result
    
    return PromptChainingResult(final_result=current_result)

# Example usage
initial_prompt = "Describe the process of photosynthesis."
chain_prompts = [
    "Explain the role of sunlight in this process.",
    "Describe the role of chlorophyll.",
    "What are the end products of photosynthesis?"
]
result = prompt_chaining(initial_prompt, chain_prompts)
print(result.final_result)

class ConditionalPromptingResult(BaseModel):
    response: str

def conditional_prompting(prompt: str, condition: str) -> ConditionalPromptingResult:
    """
    Implement conditional prompting by altering the prompt based on specific conditions.
    """
    messages = [
        {"role": "system", "content": f"You are an assistant that responds based on the following condition: {condition}"},
        {"role": "user", "content": prompt},
    ]
    
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=ConditionalPromptingResult,
        messages=messages,
    )

# Example usage
prompt = "Provide health advice."
condition = "The user is a diabetic."
result = conditional_prompting(prompt, condition)
print(result.response)

class HypotheticalPromptingResult(BaseModel):
    response: str

def hypothetical_prompting(prompt: str, scenario: str) -> HypotheticalPromptingResult:
    """
    Implement hypothetical prompting by asking the model to consider hypothetical scenarios.
    """
    messages = [
        {"role": "system", "content": "You are an assistant that explores hypothetical scenarios."},
        {"role": "user", "content": f"Consider the following hypothetical scenario: {scenario}"},
        {"role": "user", "content": prompt},
    ]
    
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=HypotheticalPromptingResult,
        messages=messages,
    )

# Example usage
prompt = "What would be the impact on global trade?"
scenario = "If all countries abolished tariffs."
result = hypothetical_prompting(prompt, scenario)
print(result.response)

class ContextAwarePromptingResult(BaseModel):
    response: str

def context_aware_prompting(prompt: str, context: str) -> ContextAwarePromptingResult:
    """
    Implement context-aware prompting by providing relevant context to the model.
    """
    messages = [
        {"role": "system", "content": "You are an assistant that uses provided context to enhance responses."},
        {"role": "user", "content": f"Context: {context}"},
        {"role": "user", "content": prompt},
    ]
    
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=ContextAwarePromptingResult,
        messages=messages,
    )

# Example usage
prompt = "Summarize the recent advancements in AI."
context = "Recent advancements include GPT-4, reinforcement learning breakthroughs, and improved natural language understanding."
result = context_aware_prompting(prompt, context)
print(result.response)

class DynamicPromptingResult(BaseModel):
    response: str

def dynamic_prompting(initial_prompt: str, follow_up: str) -> DynamicPromptingResult:
    """
    Implement dynamic prompting by adjusting the prompt based on the interaction.
    """
    messages = [
        {"role": "system", "content": "You are an assistant that dynamically adjusts prompts based on interactions."},
        {"role": "user", "content": initial_prompt},
        {"role": "assistant", "content": follow_up},
    ]
    
    return client.chat.completions.create(
        model="gpt-3.5-turbo",
        response_model=DynamicPromptingResult,
        messages=messages,
    )

# Example usage
initial_prompt = "Explain the benefits of cloud computing."
follow_up = "How does it improve scalability?"
result = dynamic_prompting(initial_prompt, follow_up)
print(result.response)