Now you understand the importance of giving specific instructions, right?
Posted: Wed Feb 05, 2025 10:41 am
Text Classification
So far, we have used simple instructions to perform tasks. As a prompt engineer, you need to provide better instructions. In addition, you will find that for more responsible usage scenarios, just providing instructions is far from enough. Therefore, you need to think about how to include relevant context and other different elements in the prompt words. Similarly, you can also provide other information, such as input data and examples.
You can experience text classification with the following examples:
We gave the instruction to classify the text, and the language model responded correctly, judging the text type as 'neutral'. What if we want the language model to respond in a specific format, for example, we want it to return neutral instead of Neutral, how can we do it? There are multiple ways to achieve this. In this case, we are mainly concerned venezuela mobile database with absolute features, so the more information contained in the prompt word, the better the response result will be. We can use the following example to correct the response result:
Perfect! This time the model returned neutral, which is the specific label we wanted. The examples in the prompt allow the model to give a more specific response. Sometimes it is important to give specific instructions, as you can see in the following example:
dialogue
We can conduct more interesting experiments by providing prompt words to guide how large language models should respond. This is particularly useful when building conversational systems such as customer service chatbots.
So far, we have used simple instructions to perform tasks. As a prompt engineer, you need to provide better instructions. In addition, you will find that for more responsible usage scenarios, just providing instructions is far from enough. Therefore, you need to think about how to include relevant context and other different elements in the prompt words. Similarly, you can also provide other information, such as input data and examples.
You can experience text classification with the following examples:
We gave the instruction to classify the text, and the language model responded correctly, judging the text type as 'neutral'. What if we want the language model to respond in a specific format, for example, we want it to return neutral instead of Neutral, how can we do it? There are multiple ways to achieve this. In this case, we are mainly concerned venezuela mobile database with absolute features, so the more information contained in the prompt word, the better the response result will be. We can use the following example to correct the response result:
Perfect! This time the model returned neutral, which is the specific label we wanted. The examples in the prompt allow the model to give a more specific response. Sometimes it is important to give specific instructions, as you can see in the following example:
dialogue
We can conduct more interesting experiments by providing prompt words to guide how large language models should respond. This is particularly useful when building conversational systems such as customer service chatbots.