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## Table of Contents
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## Table of Contents
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- [Guides](#guides)
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- [Papers](#papers)
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- [Papers](#papers)
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- [Tools & Libraries](#tools--libraries)
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- [Tools & Libraries](#tools--libraries)
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- [Datasets](#datasets)
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- [Datasets](#datasets)
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- [Blog, Guides, Tutorials and Other Readings](#blog-guides-tutorials-and-other-readings)
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- [Blog, Guides, Tutorials and Other Readings](#blog-guides-tutorials-and-other-readings)
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## Guides
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The following are a set of guides on prompt engineering.
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- [Prompts Introduction](/guides/prompts-intro.md)
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## Papers
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## Papers
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#### (Sorted by Release Date)
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#### (Sorted by Release Date)
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This guide covers the basics of standard prompts to provide a rough idea on how to use prompts to interact and instruct large language models (LLMs).
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All examples are tested with `text-davinci-003` (using OpenAI's playground) unless otherwise specified. It uses the default configurations, e.g., `temperature=0.7` and `top-p=1`.
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Before starting with some basic examples, keep in mind that your results may vary depending on the version of LLM you are using.
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---
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## Basic Prompt
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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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```
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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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```
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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 instructing 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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We will cover more of these capabilities in this guide but also cover other areas of interest such as advanced prompting techniques and research topics around prompt engineering.
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