Let's look at a simple example:
Posted: Wed Feb 05, 2025 10:10 am
You can get a lot of results with a simple prompt, but the quality of the results is related to the amount and completeness of the information you provide. Prompts can contain information to pass to the model, such as instructions, questions, etc., or they can contain detailed information such as context, inputs, or examples to better guide the model and get better results.
Prompt word
The sky is
Output
blue.
As shown in the above example, the language model completes hungary mobile database the continuation based on the given context "The sky is". However, the output may be unexpected or exceed our requirements.
To achieve more specific goals, we need to provide more background or explanation information, such as:
Prompt word
Output
so beautiful today.
Is the result better? Here we explicitly ask the model to complete the sentence, so the output matches our input exactly. Prompt engineering is the study of how to design the best prompt words to guide the language model to complete the task efficiently.
Prompt word
The sky is
Output
blue.
As shown in the above example, the language model completes hungary mobile database the continuation based on the given context "The sky is". However, the output may be unexpected or exceed our requirements.
To achieve more specific goals, we need to provide more background or explanation information, such as:
Prompt word
Output
so beautiful today.
Is the result better? Here we explicitly ask the model to complete the sentence, so the output matches our input exactly. Prompt engineering is the study of how to design the best prompt words to guide the language model to complete the task efficiently.