Prompt engineering is the process of creating inputs for AI models to improve the output for a given task. A prompt is a broad instruction that triggers an AI model to generate content; it could be a statement, a block of code, or a string of words. After receiving the prompt or input, an AI model produces an output in response. The quality of the output can vary considerably depending on the prompt provided. Prompt engineering aims to improve the use of AI models by determining the best way to write or structure prompts for specific tasks. This process includes selecting the appropriate data type and formatting it so the model can understand and use it to learn. Prompt engineering creates higher-quality training data to enable the AI model to make accurate outputs. With the wider adoption of generative AI models, prompt engineering is becoming an important field, determining input methods that yield desirable and useful results.
The primary means of communication between users and generative AI models is text, and prompt engineering is closely linked to natural language processing (NLP) and how machines decipher the meaning behind a piece of text. Prompts typically have an instruction or question, and they may also contain input data and examples. Successful prompt engineering determines the best way to combine these elements for a given task.
While prompt engineering varies depending on the model and the type of content it generates (e.g., text, image, etc.), prompt engineers have developed a general set of principles to improve the output quality from generative AI models. These include the following:
- Trying multiple formulations of the prompt to get the best results
- Providing context and examples
- Making sure the instructions come before the context
- Using clear and brief prompts and avoiding unnecessary words
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Prompt-Engineering-Guide: Guides, papers, lecture, and resources for prompt engineering