Welcome to this lesson on enhancing prompt engineering with examples. In previous lessons, we discussed the importance of structured prompts and defining constraints. Today, we'll explore how examples can significantly improve the quality and format of responses from Large Language Models (LLMs). By the end of this lesson, you'll understand how to effectively use examples to guide LLMs in producing structured and relevant answers.

Let's start by examining a basic prompt without any examples or constraints. This will help us understand the limitations of such prompts.

Without any examples or constraints, the LLM might generate questions that are not well-structured or relevant. The lack of guidance can lead to varied formats and potentially incorrect or unclear options.

Here is an example of LLM's answer:

Now, let's see how including an example can improve the LLM's response. An example provides a clear template for the LLM to follow, ensuring consistency and relevance.

By including this example, we guide the LLM to format its responses similarly.

Next, let's introduce constraints to further refine the LLM's output. Constraints help ensure that the LLM adheres to specific requirements, improving the precision of its responses.

These constraints instruct the LLM to avoid repeating the example question and to focus solely on generating new content without unnecessary introductions or conclusions.

In this lesson, we explored how examples and constraints can enhance the quality of LLM responses. By providing a clear example, we guide the LLM to follow a specific format, while constraints ensure precision and relevance. As you move on to the practice exercises, experiment with different examples and constraints to see how they impact the LLM's output. This hands-on practice will solidify your understanding and help you become proficient in prompt engineering.