diff --git a/pages/introduction/basics.en.mdx b/pages/introduction/basics.en.mdx index ae50442..940049e 100644 --- a/pages/introduction/basics.en.mdx +++ b/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: ``` ? @@ -55,7 +55,7 @@ or ``` -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: ? @@ -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: ``` ? @@ -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.