Writing prompts for large language models (LLMs) is an art and a science. Much like how a skilled artist uses brushes and colors to create a masterpiece, a proficient engineer uses words and context to craft prompts that yield optimal results. In this blog post, we’ll delve into the best practices for writing effective prompts, presented in a story-like fashion to keep things engaging and easy to understand.

What is a Prompt?​

Imagine you're a wizard, and the prompt is your spell. A well-crafted spell produces a powerful effect, while a poorly crafted one might not work as intended. Similarly, a well-written prompt can make the difference between receiving a precise, helpful response and getting an ambiguous or irrelevant one.

Prompts are the instructions given to LLMs, guiding them to generate the desired output. Think of them as the starting point of a conversation, setting the stage for the model to understand and respond appropriately.

Best Practices for Writing Prompts​

Best Practice 1: Quick Start with a Meta-Prompt​

To get started quickly, you can write a meta-prompt—a prompt that asks the model to help you write a prompt. This approach is akin to teaching a student how to frame questions to get the best answers from their teacher. This technique can save you time and provide a strong foundation for more specific prompts.

Meta-Prompt Example:

You are an expert in writing prompts for large language models. Explain how to write an effective prompt that achieves the desired result. Include examples and common pitfalls to avoid.

Using this meta-prompt, the LLM can provide a comprehensive guide on writing effective prompts, which you can then refine for your specific task.

Best Practice 2: Be Clear and Specific​

Clarity is king when it comes to prompts. The more specific and clear your instructions, the better the response. Ambiguity is the enemy; it can lead to vague or incorrect answers. This is similar to giving directions; the clearer you are, the more likely you’ll arrive at your desired destination.

Example of a Clear Prompt:

Write a short story about a young wizard who discovers their magical abilities in a small village.

Example of an Ambiguous Prompt:

Tell me a story.

Notice how the first prompt sets clear expectations about the story's content, while the second is too vague.

Best Practice 3: Provide Context​

Context is crucial. Imagine asking a friend to fetch something from a room without telling them what or where it is. Similarly, LLMs need context to generate relevant responses. Providing context helps the model understand the background and generate more accurate responses.

Example of Providing Context:

In the style of J.K. Rowling, write a short story about a young wizard who discovers their magical abilities in a small village.

By adding context about the style, you guide the model to produce a more tailored and coherent response.

Best Practice 4: Specify the Format​

If you need the output in a specific format, make it clear in your prompt. This ensures that the model understands your requirements and structures the response accordingly. This is particularly useful when you need structured data or a specific layout.

Example of Format Specification:

Create a bulleted list of the top five benefits of learning to code.

Expected Response:

- Enhances problem-solving skills
- Opens up job opportunities
- Encourages creativity
- Provides a high earning potential
- Facilitates continuous learning

Best Practice 5: Iterate and Refine​

Writing prompts is an iterative process. Start with a draft, test it, and refine based on the responses you receive. Even the best prompts can often be improved. Think of it like editing a piece of writing; the first draft is rarely perfect, and revisions help hone the final product.

Iteration Example:

First Draft:

Explain how a computer works.

Refined Draft:

Explain in simple terms how a computer processes data, including the roles of the CPU, memory, and storage.

Best Practice 6: Use Examples and Analogies​

Examples and analogies can help clarify complex instructions and make the prompt more relatable. They act as bridges between abstract concepts and the model's understanding, ensuring clearer communication.

Example with Analogy:

Explain how a neural network works, using the analogy of how the human brain learns from experience.

Best Practice 7: Anticipate Possible Misunderstandings​

Consider potential misunderstandings the model might have and address them in your prompt. By preempting these issues, you can craft a more precise prompt that avoids common pitfalls.

Example of Anticipating Misunderstandings:

Describe the steps to bake a cake, focusing on the sequence of mixing ingredients, baking time, and cooling period. Avoid discussing decoration techniques.

Best Practice 8: Include Instructions for Length​

If you need a response of a particular length, specify it in your prompt. This helps the model generate a response that meets your expectations in terms of detail and comprehensiveness.

Example of Length Specification:

Write a 200-word summary of the plot of 'The Great Gatsby.'

Best Practice 9: Test with Multiple Models​

Different models might interpret prompts slightly differently. Test your prompts with multiple models to ensure consistency and accuracy. This approach helps identify any variations in responses and refine prompts for broader applicability.

Best Practice 10: Seek Feedback​

Engage with a community or colleagues to get feedback on your prompts. Peer review can provide insights you might have missed and help improve the quality of your prompts.

Conclusion​

Writing effective prompts is like crafting a spell: it requires clarity, specificity, context, and refinement. By following these best practices, you can enhance the performance of LLMs and achieve more accurate and useful responses. Start with a meta-prompt, be clear and specific, provide context, specify the format, and always iterate and refine. Use examples and analogies, anticipate misunderstandings, include length instructions, test with multiple models, and seek feedback to continually improve your prompt-crafting skills.

Happy prompt crafting!

Related reading:

• How to Efficiently Write Long and Stable Prompts for Large Language Models

Introduction​

In the world of AI, particularly when working with large language models (LLMs) like GPT-4, the prompt you provide plays a crucial role in determining the quality and stability of the model's output. Writing effective prompts, especially longer ones, can be a bit of a challenge, but mastering this skill can significantly enhance the performance and reliability of your AI applications. This blog post will guide you through the best practices for crafting long, stable prompts, ensuring that your interactions with LLMs are as effective and consistent as possible.

What is Prompt​

A prompt is a piece of text or instruction provided to a language model to generate a desired response. It serves as the initial input that guides the model's output. The quality and clarity of the prompt significantly influence the relevance, accuracy, and coherence of the model's responses.

Prompts can vary in length and complexity, from simple questions or statements to detailed instructions. A well-crafted prompt provides sufficient context and specific guidance, reducing ambiguity and enhancing the model's ability to produce useful and relevant outputs. For instance, a simple prompt like "Explain the importance of water conservation" can yield general responses, while a more detailed prompt specifying the aspects to cover, such as environmental, economic, and social impacts, can lead to a more comprehensive and structured output.

Effective prompt design is crucial, especially when dealing with complex tasks. It involves clarity in language, providing necessary context, and being specific about the desired outcome. By mastering prompt engineering, users can leverage the full potential of large language models, ensuring that the responses generated are both insightful and reliable.

Understanding the Importance of Prompts​

Prompts are the initial input you provide to a language model to generate a desired output. They set the stage for the model's response and heavily influence the quality, relevance, and coherence of the output. A well-crafted prompt can lead to insightful, accurate, and useful responses, while a poorly designed one can result in vague, incorrect, or irrelevant answers.

Why Long Prompts?​

Longer prompts are often necessary when dealing with complex tasks that require detailed instructions or multiple pieces of information. They provide the model with sufficient context and guidance, reducing ambiguity and improving the relevance of the responses. However, writing long prompts requires careful consideration to maintain clarity and coherence.

Key Principles for Writing Effective Prompts​

Clarity​

Clarity is the cornerstone of an effective prompt. A clear prompt ensures that the model understands exactly what you are asking for.

• Use Simple Language: Avoid jargon or overly complex sentences. The goal is to make your prompt easy to understand.
• Be Direct: Clearly state your request or question. Avoid unnecessary information that could confuse the model.

Context​

Context provides the background information the model needs to generate a relevant response.

• Background Information: Provide any necessary details that the model might need to understand the prompt fully.
• Relevant Details: Include only the information that directly impacts the task at hand. Irrelevant details can distract the model and lead to less accurate responses.

Specificity​

Specificity ensures that the model knows exactly what you want.

• Define the Scope: Clearly outline the scope of the task or question. Specify any constraints or requirements.
• Ask Precise Questions: Instead of asking broad questions, break them down into more specific sub-questions.

Structuring Long Prompts​

When dealing with longer prompts, structuring your content becomes even more crucial. A well-structured prompt helps maintain clarity and coherence, making it easier for the model to understand and respond accurately.

Introduction​

Introduce the Task or Question

• Set the Stage: Begin with a brief introduction that outlines the main task or question. This helps the model understand the overall objective.
• Provide Necessary Background: Include any essential background information that is necessary for understanding the task.

Body​

Break Down the Task

• Divide into Sections: For complex tasks, divide the prompt into clear sections or steps. This helps the model follow the logical flow of information.
• Use Bullet Points or Lists: Organizing information in bullet points or lists can make it easier for the model to process and understand.

Conclusion​

Summarize and Reinforce

• Summarize the Key Points: End with a brief summary that reinforces the main points of the prompt.
• Reiterate the Objective: Clearly restate the desired outcome or the main question to keep the model focused.

Structured Template Example​

Below is a commonly used Markdown template for prompts, with various sections that can be expanded, contracted, or customized as needed.

# Role

Define the roles of a large model, such as specialists in specific areas.

# Background

Provide background information pertinent to the task.

# Requirements

Task requirements:

- Requirement 1;
- Requirement 2;
- Requirement 3;
  ...

# Work Steps

- Step 1;
- Step 2;
- Step 3;
  ...

Ensuring Stability in Model Outputs​

Stability in the outputs of a language model means that the responses are consistent and reliable over multiple interactions. Achieving this requires a combination of clear prompts, consistency in wording, and iterative testing.

Consistency​

Maintain Consistent Wording and Structure

• Use Consistent Terminology: Ensure that you use the same terms and phrases throughout your prompt to avoid confusing the model.
• Standardize Prompt Structure: Develop a standard structure for your prompts to make them more predictable for the model.

Reinforcement​

Reinforce Key Points

• Repeat Important Information: Reiterate key details or instructions to reinforce their importance.
• Bold font for key parts: When using Markdown syntax to format Prompts, use bold for the parts that require emphasis.
• Capitalization of key parts: When writing Prompts in English, capitalization can be used to emphasize the parts that need to be emphasized, for example: "MUST..., DON NOT..."

Provide Examples​

Provide Positive Examples​

• Provide positive examples to illustrate the desired response or format.

Provide Negative Examples​

• Provide negative examples to illustrate the undesired response or format.

Testing and Iteration​

Iterative Testing for Improvement

• Test Different Versions: Experiment with different versions of your prompt to see which one yields the best results.
• Refine and Optimize: Based on the model's responses, refine and optimize your prompt to improve clarity and effectiveness.

Examples and Case Studies​

Example 1: Writing a Research Paper​

Initial Prompt:

Write a research paper on climate change.

Refined Long Prompt:

Please write a detailed research paper on climate change, focusing on the following aspects:

1. Introduction
   - Define climate change.
   - Explain its significance.
2. Causes of Climate Change
   - Natural causes.
   - Human-induced causes.
3. Effects of Climate Change
   - Environmental impact.
   - Economic impact.
   - Social impact.
4. Mitigation Strategies
   - Renewable energy solutions.
   - Policy recommendations.
5. Conclusion
   - Summarize key points.
   - Provide future outlook and recommendations.

Ensure that the paper is well-researched, with credible sources cited. The length should be between 3000 to 4000 words.

Conclusion​

Writing effective long prompts for large language models is a critical skill that can significantly enhance the quality and stability of the model's output. By focusing on clarity, context, and specificity, and by structuring your prompts carefully, you can guide the model to produce more accurate and relevant responses. Remember to maintain consistency, reinforce key points, and iterate through testing to refine your prompts further.

By mastering these techniques, you will be able to leverage the full potential of large language models, making them powerful tools in your AI toolkit.

Happy prompting!

Let's continue exploring and improving our prompt-writing skills together!

如何高效编写内容较长且输出稳定的 Prompt

介绍​

在 AI 的世界中，特别是在使用像 GPT-4 这样的大型语言模型（LLM）时，您提供的 Prompt 在确定模型输出的质量和稳定性方面起着关键作用。编写有效的 Prompt，特别是较长的 Prompt，可能有些挑战，但掌握这一技能可以显著提高您的 AI 应用程序的性能和可靠性。这篇博客将指导您如何编写长且稳定的 Prompt，确保您与 LLM 的互动尽可能高效和一致。

Prompt 是什么​

Prompt（提示词）是提供给语言模型的一段文字或指令，用于生成期望的响应。它是引导模型输出的初始输入。Prompt 的质量和清晰度对模型响应的相关性、准确性和连贯性有着显著影响。

Prompt 可以有不同的长度和复杂度，从简单的问题或陈述到详细的指示。一个精心设计的 Prompt 提供了足够的上下文和具体的指导，减少了歧义，并增强了模型生成有用且相关输出的能力。例如，一个简单的 Prompt 如“解释水资源保护的重要性”可以产生一般性的回答，而一个更详细的 Prompt，指定需要涵盖的方面，如环境、经济和社会影响，可以导致更全面和结构化的输出。

有效的 Prompt 设计至关重要，特别是在处理复杂任务时。它涉及语言的清晰性，提供必要的上下文，并明确期望的结果。通过掌握 Prompt 工程，用户可以充分利用大型语言模型的潜力，确保生成的响应既有深度又可靠。

理解 Prompt 的重要性​

Prompt 是您提供给语言模型的初始输入，它决定了模型生成的输出质量。一个精心设计的 Prompt 可以导致深入、准确和有用的响应，而一个设计不良的 Prompt 可能会产生模糊、错误或不相关的回答。

为什么需要长 Prompt？​

在处理需要详细说明或包含多条信息的复杂任务时，长 Prompt 往往是必要的。它们为模型提供了足够的上下文和指导，减少了歧义，提高了响应的相关性。然而，编写长 Prompt 需要仔细考虑，以保持清晰和连贯。

编写有效 Prompt 的关键原则​

清晰​

清晰是有效 Prompt 的基石。一个清晰的 Prompt 确保模型准确理解您的要求。

• 使用简单语言：避免术语或过于复杂的句子。目标是让您的 Prompt 易于理解。
• 直接表达：明确陈述您的请求或问题。避免不必要的信息，以免混淆模型。

上下文​

上下文提供了模型生成相关响应所需的背景信息。

• 背景信息：提供模型可能需要的任何必要细节，以完全理解 Prompt。
• 相关细节：仅包含直接影响任务的信息。无关的细节可能会分散模型的注意力，导致不准确的响应。

具体​

具体确保模型确切知道您的需求。

• 定义范围：清晰概述任务或问题的范围。指定任何约束或要求。
• 问具体问题：而不是问广泛的问题，将其分解为更具体的子问题。

结构化长 Prompt​

在处理较长的 Prompt 时，结构化内容变得尤为重要。一个良好结构化的 Prompt 有助于保持清晰和连贯，使模型更容易理解和准确响应。

介绍​

介绍任务或问题

• 设置场景：以简短的介绍开始，概述主要任务或问题。这有助于模型理解总体目标。
• 提供必要背景：包括理解任务所需的任何基本背景信息。

主体​

分解任务

• 分段：对于复杂任务，将 Prompt 分为清晰的部分或步骤。这有助于模型遵循信息的逻辑流。
• 使用项目符号或列表：将信息组织成项目符号或列表形式，可以让模型更容易处理和理解。

结论​

总结和强化

• 总结关键点：以简短的总结结束，强化 Prompt 的主要点。
• 重申目标：清晰重申期望的结果或主要问题，以保持模型的注意力。

结构化模板示例​

下面是一个常用的 Markdown 格式 Prompt 模板，各主要部分可根据实际情况增删和自定义。

# 角色

定义大模型的工作角色，如某某方面专家等。

# 背景

介绍任务相关背景信息。

# 要求

任务相关要求：

- 要求 1;
- 要求 2;
- 要求 3;
  ...

# 工作步骤

- 步骤 1;
- 步骤 2;
- 步骤 3;
  ...

确保模型输出的稳定性​

模型输出的稳定性意味着响应在多次交互中是一致且可靠的。实现这一目标需要结合清晰的 Prompt、一致的措辞以及迭代测试。

一致性​

保持一致的措辞和结构

• 使用一致的术语：确保在 Prompt 中使用相同的术语和短语，以避免混淆模型。
• 标准化 Prompt 结构：开发标准结构，使其对模型更具可预测性。

强化​

强化关键点

• 重复重要信息：重申关键细节或指示，以强化其重要性。

• 重点部分字体加粗：在使用 Markdown 语法格式组织 Prompt 时，对需要强调的部分可使用**加粗。例： **重点强调内容**。

• 重点部分字体大写：对英文编写 Prompt 时，对需要强调的部分可使用大写以示强调。例： MUST..., DON NOT...。

提供示例​

提供正面示例​

• 提供正面示例以说明期望的响应或格式。

提供反面示例​

• 提供反面示例以说明不期望的响应或格式。

测试与迭代​

通过迭代测试进行改进

• 测试不同版本：尝试不同版本的 Prompt，看看哪个能产生最佳结果。
• 优化和改进：根据模型的响应，优化和改进您的 Prompt，以提高清晰度和有效性。

示例和案例研究​

示例 1：撰写研究论文​

初始 Prompt：

撰写一篇关于气候变化的研究论文。

优化后的长 Prompt：

请撰写一篇详细的关于气候变化的研究论文，重点包括以下方面：

1. 介绍
   - 定义气候变化。
   - 解释其重要性。
2. 气候变化的原因
   - 自然原因。
   - 人为原因。
3. 气候变化的影响
   - 环境影响。
   - 经济影响。
   - 社会影响。
4. 缓解策略
   - 可再生能源解决方案。
   - 政策建议。
5. 结论
   - 总结关键点。
   - 提供未来展望和建议。

确保论文经过充分研究，并引用可信的来源。长度应在 3000 至 4000 字之间。

结论​

编写有效的长 Prompt 是提高大型语言模型输出质量和稳定性的重要技能。通过关注清晰、上下文和具体性，并通过仔细结构化您的 Prompt，您可以引导模型生成更准确和相关的响应。记住要保持一致性，强化关键点，并通过测试和迭代来优化您的 Prompt。

通过掌握这些技术，您将能够充分利用大型语言模型，使其成为您 AI 工具箱中的强大工具。

Prompt 愉快！

让我们一起继续探索和改进我们的 Prompt 编写技能！