Suppose I want to know information about antibiotics, I can give a prompt like this:
Tips:
Explain antibiotics
A:
Output:
In the question-answer format, "A:" is a clear prompt iceland mobile database format. In this example, I use it to prompt the model that I want to further explain the concept. In the following examples, we will explore its specific uses. Now suppose we feel that the model gives too much information and want to further refine it. We can instruct the model to summarize the relevant content for us in one sentence:
This is an example of the model trying to summarize the paragraph in one sentence without paying much attention to the accuracy of the output above. Regarding the accuracy of the above, we can further improve it through instructions or instructions, which we will explore in the subsequent guides. At this point, you can pause and experiment to see if you can get better results.
Information Extraction
Language models can not only be trained to perform natural language generation related tasks, but can also be used to perform text classification and other various natural language processing NLP tasks.
There are many ways we could improve on the above results, but the current approach is already very useful.
It should be obvious by now that you can perform different tasks by simply instructing a language model. AI developers also use this capability to build powerful products and experiences.