Merge pull request #155 from taolicd/patch-6

Update basics.en.mdx

pages/introduction/basics.en.mdx

@@ -2,7 +2,7 @@
 
 ## Basic Prompts
 
-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.
+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 include 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.
 
 Let's get started by going over a basic example of a simple prompt:
 
@@ -18,9 +18,9 @@ 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 given the context `"The sky is"`. The output might be unexpected or far from the task we want to accomplish. 
+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 you want to accomplish. 
 
-This basic example also highlights the necessity to provide more context or instructions on what specifically we want to achieve.
+This basic example also highlights the necessity to provide more context or instructions on what specifically you want to achieve.
 
 Let's try to improve it a bit:
 
@@ -37,13 +37,13 @@ The sky is
 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 designing optimal prompts to instruct the model to perform a task is what's referred to as **prompt engineering**. 
+Is that better? Well, you told the model to complete the sentence so the result looks a lot better as it follows exactly what you 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**. 
 
 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.
 
 ## Prompt Formatting
 
-We have tried a very simple prompt above. A standard prompt has the following format:
+You have tried a very simple prompt above. A standard prompt has the following format:
 
 ```
 <Question>?
@@ -55,7 +55,7 @@ or
 <Instruction>
 ```
  
-This can be formatted into a question answering (QA) format, which is standard in a lot of QA datasets, as follows:
+You can format this into a question answering (QA) format, which is standard in a lot of QA datasets, as follows:
 
 ```
 Q: <Question>?
@@ -64,7 +64,7 @@ A:
 
 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. 
 
-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:
+Given the standard format above, one popular and effective technique to prompting is referred to as *few-shot prompting* where you provide exemplars (i.e., demonstrations). You can format few-shot prompts as follows:
 
 ```
 <Question>?
@@ -111,4 +111,4 @@ What a horrible show! //
 Negative
 ```
 
-Few-shot prompts enable in-context learning which is the ability of language models to learn tasks given a few demonstrations.
+Few-shot prompts enable in-context learning, which is the ability of language models to learn tasks given a few demonstrations.