add first guide

README.md

@@ -14,12 +14,18 @@ Announcements:
 
 ## Table of Contents
 
+- [Guides](#guides)
 - [Papers](#papers)
 - [Tools & Libraries](#tools--libraries)
 - [Datasets](#datasets)
 - [Blog, Guides, Tutorials and Other Readings](#blog-guides-tutorials-and-other-readings)
 
 
+## Guides
+The following are a set of guides on prompt engineering. 
+
+- [Prompts Introduction](/guides/prompts-intro.md)
+
 ## Papers
 #### (Sorted by Release Date)
 

guides/prompts-intro.md

@@ -0,0 +1,48 @@
+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). 
+
+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`.
+
+Before starting with some basic examples, keep in mind that your results may vary depending on the version of LLM you are using. 
+
+---
+
+## Basic Prompt
+
+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`. 
+
+Here is a basic example of a simple prompt:
+
+```
+The sky is
+```
+
+Output:
+```
+blue
+
+The sky is blue on a clear day. On a cloudy day, the sky may be gray or white.
+```
+
+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. 
+
+This basic example also highlights the necessity to provide more context or instructions on what specifically we want to achieve.
+
+Let's try to improve it a bit:
+
+```
+Complete the sentence: 
+
+The sky is
+```
+
+Output
+
+```
+ so  beautiful today.
+```
+
+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**. 
+
+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.
+
+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.