Introduction
Welcome to our Awesome Prompt Engineering Github Repository! Prompt engineering is an essential component of natural language processing (NLP) that involves designing prompts or input queries for AI models that generate natural language text. The quality of these prompts significantly impacts the quality and relevance of the output text. This field is becoming increasingly important as AI models are used more frequently in various applications, such as chatbots, virtual assistants, and text summarization. Effective, prompt engineering can improve the performance of these models and make them more useful for a wide range of applications. We are excited to provide a resource for developers and researchers to learn more about this exciting field and to contribute to its continued growth and development.
Examples
Prompt 1: Sentiment
What is the sentiment of the following product review, which is delimited with triple backticks?
{text}
Prompt 2: Sentiment
Identify a list of emotions the writer of the following review expresses, which is delimited with triple backticks?
{text}
Prompt 3: Translation
Translate the following text to French and German, and English:
{text}
Prompt 4: Translation
Translate the following dictionary from JSON to a HTML table:
{text}
Best Practices
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Use clear and concise language: One of the key benefits of prompt engineering is its ability to generate natural language responses. To achieve this, it's important to use clear and concise language in your prompts. Avoid using jargon or technical terms that might be unfamiliar to your users.
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Use context to guide the prompts: When generating prompts, it's important to take into account the context in which they will be used. This can include things like user demographics, user preferences, and the task at hand. By incorporating contextual information into your prompts, you can ensure that they are relevant and useful.
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Test and validate your prompts: It's important to test and validate your prompts to ensure that they are accurate, relevant, and useful. This can involve things like conducting user testing, analyzing user feedback, and measuring the effectiveness of your prompts. By continually refining and improving your prompts, you can ensure that they provide the best possible user experience.
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Document your prompts: It's important to document your prompts so that other developers can understand how to use them effectively. This can include things like providing clear instructions on how to integrate your prompts into their projects, explaining how to use different prompt settings and configurations, and providing examples of how to use your prompts in real-world scenarios.
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Follow coding standards and best practices: When implementing prompt engineering techniques, it's important to follow coding standards and best practices to ensure that your code is maintainable, scalable, and efficient. This can include things like using consistent naming conventions, avoiding code duplication, and commenting your code to make it more readable and understandable.
Notes
Feedback and suggestions are welcome!
For more information about prompt engineering, including frequently asked questions about DALL-E and CHATGPT, please visit our FAQ section.