Welcome to your first lesson in advanced prompt engineering! In this course, you will learn how to get better results from language models by crafting your prompts more thoughtfully. Even small changes in how you write your instructions can significantly affect the quality and accuracy of the AI's responses.

In this lesson, we will focus on three practical tips:

• How to highlight important instructions in your prompts
• How to use consequences (both negative and positive) to guide the model's behavior

By the end of this lesson, you will be able to apply these techniques to make your prompts clearer and more effective.

When you give instructions to a language model, it sometimes misses or ignores the most critical parts. A straightforward way to help the model focus is to highlight key instructions.

Let's start with a basic prompt:

This is clear, but what if you want to make sure the model does not miss a specific instruction, like summarizing in exactly three sentences? You can highlight the important parts:

By using CAPSLOCK for the most important instruction, you draw the model's attention to it. You can also use other formatting, like making some words bold with markdown. Example:

Why does this work?
Language models are trained to pay attention to emphasis, just like humans. Highlighting key instructions helps the model understand what is most important in your prompt.

Sometimes, even with highlighted instructions, the model might not follow your directions perfectly. Another technique is to mention the consequences if the instruction is not followed. This can make the instruction stand out even more.

Let's build up an example step by step.

Start with a simple instruction:

This is a direct statement, but it can be made even stronger by adding a consequence:

Here, the consequence (you will be fired) is a strong negative outcome. This makes the model more likely to pay attention and follow the instructions closely.

You can also combine highlighting and consequences:

Why does this work?
Language models are trained on a lot of text in which consequences matter. When you mention a consequence, the model is more likely to treat the instruction as important and try harder to follow it.

Negative consequences are not the only way to guide the model. If you prefer to use positive reinforcement, you can offer a reward if the instruction is followed.

Let's see how this works, step by step.

Start with a basic instruction:

Now, add a positive consequence:

This tells the model that there is a reward for following the instructions. Even though the model does not actually receive a tip, it is trained to respond to positive outcomes and will try to do a better job.

Why does this work?
Language models are trained on text in which rewards and praise are common. Positive reinforcement can make the model more likely to follow your instructions carefully.

In this lesson, you learned three practical tips for making your prompts more effective:

• Highlighting important instructions using CAPSLOCK or formatting
• Using consequences (threats or warnings) to make instructions stand out
• Using rewards and positive reinforcement to encourage the model to follow your instructions

These techniques help you get clearer, more accurate responses from language models. In the next section, you will get a chance to practice these strategies by writing your own prompts and seeing how the model responds. Try experimenting with different ways to highlight, warn, or reward, and notice how the model's answers change. Good luck!