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https://github.com/dair-ai/Prompt-Engineering-Guide
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115 lines
3.2 KiB
Plaintext
115 lines
3.2 KiB
Plaintext
# Basics of Prompting
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## Basic Prompts
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You can achieve a lot with simple prompts, but the quality of results depends on how much information you provide it and how well-crafted it is. A prompt can contain information like the *instruction* or *question* you are passing to the model and including other details such as *context*, *inputs*, or *examples*. You can use these elements to instruct the model better and as a result get better results.
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Let's get started by going over 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 given 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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## Prompt Formatting
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We have tried a very simple prompt above. A standard prompt has the following format:
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```
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<Question>?
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```
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or
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```
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<Instruction>
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```
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This can be formatted into a question answering (QA) format, which is standard in a lot of QA datasets, as follows:
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```
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Q: <Question>?
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A:
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```
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When prompting like the above, it's also referred to as *zero-shot prompting*, i.e., you are directly prompting the model for a response without any examples or demonstrations about the task you want it to achieve. Some large language models do have the ability to perform zero-shot prompting but it depends on the complexity and knowledge of the task at hand.
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Given the standard format above, one popular and effective technique to prompting is referred to as *few-shot prompting* where we provide exemplars (i.e., demonstrations). Few-shot prompts can be formatted as follows:
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```
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<Question>?
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<Answer>
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<Question>?
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<Answer>
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<Question>?
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<Answer>
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<Question>?
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```
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The QA format version would look like this:
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```
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Q: <Question>?
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A: <Answer>
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Q: <Question>?
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A: <Answer>
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Q: <Question>?
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A: <Answer>
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Q: <Question>?
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A:
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```
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Keep in mind that it's not required to use QA format. The prompt format depends on the task at hand. For instance, you can perform a simple classification task and give exemplars that demonstrate the task as follows:
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*Prompt:*
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```
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This is awesome! // Positive
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This is bad! // Negative
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Wow that movie was rad! // Positive
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What a horrible show! //
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```
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*Output:*
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```
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Negative
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```
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Few-shot prompts enable in-context learning which is the ability of language models to learn tasks given a few demonstrations.
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