The Gist
- Basics mastered. Prompt engineering fine-tunes text for AI models, aiding marketers in content creation.
- Utilizing AI. Effective prompt engineering allows AI models to perform various roles, with different models suited to specific tasks.
- Guiding AI. Word prioritization impacts AI interpretation. Clear, unbiased, appropriately specific prompts yield better results.
Prompt engineering is the process of fine-tuning the text prompt that a user submits to generative AI models in order for them to perform specific tasks. By using prompt engineering, marketers can more effectively leverage generative AI to improve marketing campaigns and create engaging content.
This article will look at the basics of prompt engineering, and how it can be used to obtain specific results that can be used for marketing, content production, advertising and customer experience.
How Does Prompt Engineering Work?
A prompt is the text that is entered by a user into a text box on a generative AI website or application. Prompts are used to direct the large language model to return a response that is based on the prompt. Because generative AI forms the response based on the most likely way that a sentence is completed, prompts can be simple, such as “The ocean is.” Such a prompt would return a result that completes the sentence.
Generative AI models such as ChatGPT, Bing and Bard are able to research extensive topics and provide users with a summary of web pages, documents and even their own research. To get the most from the large language models, however, users must craft the prompt they use to make requests. This is known as prompt engineering.
There are several different types of prompt engineering, including Chain of Thought (CoT), zero-shot, one-shot and few-shot prompting.
- Zero-shot – A zero-shot prompt is a query for a machine learning model in zero-shot