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.
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.
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.
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 you provide exemplars (i.e., demonstrations). You can format few-shot prompts as follows:
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:
