40 lines
1.4 KiB
Plaintext
40 lines
1.4 KiB
Plaintext
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# Basic Prompts
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You can already achieve a lot with prompts, but the quality of results depends on how much information you provide it. A prompt can contain information like the `instruction` or `question` you are passing to the model and including other details such as `inputs` or `examples`.
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Here is a basic example of a simple prompt:
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*Prompt*
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```
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The sky is
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```
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*Output:*
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```
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blue
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The sky is blue on a clear day. On a cloudy day, the sky may be gray or white.
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```
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As you can see, the language model outputs a continuation of strings that make sense give the context `"The sky is"`. The output might be unexpected or far from the task we want to accomplish.
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This basic example also highlights the necessity to provide more context or instructions on what specifically we want to achieve.
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Let's try to improve it a bit:
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*Prompt:*
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```
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Complete the sentence:
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The sky is
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```
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*Output:*
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```
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so beautiful today.
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```
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Is that better? Well, we told the model to complete the sentence so the result looks a lot better as it follows exactly what we told it to do ("complete the sentence"). This approach of designing optimal prompts to instruct the model to perform a task is what's referred to as **prompt engineering**.
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The example above is a basic illustration of what's possible with LLMs today. Today's LLMs are able to perform all kinds of advanced tasks that range from text summarization to mathematical reasoning to code generation.
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