For example, to ask for explanation of the prompt project
Posted: Thu Feb 06, 2025 3:04 am
Emphasize expected behavior
When constructing prompts, it's better to tell the model explicitly what you want it to do, rather than telling it what not to do. This approach is usually more specific and more likely to produce the desired response from the model.
Let’s take a chatbot that recommends movies. First, the less-than-ideal version:
Prompt word example
In the previous section, we introduced how to enable the basic usage of large language models.
In this section, we will give more examples of how to iceland mobile database use prompt words to complete different tasks and the important concepts involved. Generally speaking, learning concepts through examples is the best way. Below, we will use examples to illustrate how to use sophisticated prompt words to perform different types of tasks.
Text summary
A standard task in natural language generation is text summarization. Text summarization can involve different styles and domains. In fact, one of the most promising application scenarios of language models is to be able to quickly summarize the main idea and related concepts of an article in an easy-to-understand way. We can try a simple summarization task using a prompt word.
When constructing prompts, it's better to tell the model explicitly what you want it to do, rather than telling it what not to do. This approach is usually more specific and more likely to produce the desired response from the model.
Let’s take a chatbot that recommends movies. First, the less-than-ideal version:
Prompt word example
In the previous section, we introduced how to enable the basic usage of large language models.
In this section, we will give more examples of how to iceland mobile database use prompt words to complete different tasks and the important concepts involved. Generally speaking, learning concepts through examples is the best way. Below, we will use examples to illustrate how to use sophisticated prompt words to perform different types of tasks.
Text summary
A standard task in natural language generation is text summarization. Text summarization can involve different styles and domains. In fact, one of the most promising application scenarios of language models is to be able to quickly summarize the main idea and related concepts of an article in an easy-to-understand way. We can try a simple summarization task using a prompt word.