We used the word “prompt” while discussing how users interact with ChatGPT and other Open AI models. In this chapter, we will discuss the significance of “prompt engineering” to enhance model’s accuracy.

The way prompts are designed and crafted influence the output of the model in the following ways −

• A well-designed prompt can guide the model to produce relevant and precise output.

• Whereas a poorly designed prompt may result in irrelevant or confusing output.

This is just a basic chapter on how you can use different types of prompts in ChatGPT to get the exact information you are looking for. We would like you to refer our tutorial where you will find extensive detail on this topic.

Prompts and Their Significance

Generative AI models can create various things like poems, stories, images, and code as per user request. However, to get the output we want, we have to give these models the right instructions, known as prompts.

Prompts, which mainly refer to a segment of text in natural language, are like the guide for the generative AI model”s output, affecting its tone, style, and overall quality. In fact, prompts are the only way a user can direct the output generated by these models.

Types of Prompts for ChatGPT

The categories of prompts used in ChatGPT act as guidelines or instructions provided to GPT to steer and control specific kinds of responses and conversations. Let”s explore the commonly used prompts and understand the ways they can be beneficial.

Instructional Prompts

Instructional prompts are commands that direct the model with specific instructions on the desired format or information to include in the response.

Let’s see an example below −

Roleplay Prompts

Roleplay prompts are commands that frame the input as if the model is a character or has specific role, guiding its response accordingly.

Consider the following example −

Question-Answer Prompts

As the name suggests, QA prompts are commands that pose questions to the model to elicit informative or creative answers.

Take a look at the following example −

Contextual Prompts

Contextual prompts provide context or background information to guide the model’s understanding and response.

Check the following example −

Creative Storytelling Prompts

Creative storytelling prompts encourage the model to generate imaginative or creative narratives by setting up scenarios or story elements.

Observe how the following prompt works in this example −

Conditional Prompts

Conditional prompts specify conditions or constraints for the response to guide the model”s output in a particular direction.

Explore the following example −

Comparison Prompts

Comparison prompts ask the model to compare or contrast different concepts, ideas, or scenarios. Consider the following example −

Instructive Prompts

Instructive prompts clearly instruct the model on the desired behavior or approach in its response. Take a look at the following example −

Principles of Well-Defined Prompts

In the earlier discussion, we emphasized the significance of prompt engineering in influencing model output. Now, let”s delve into recommended practices for improving your prompts and identify some practices to avoid.

Ensure Clarity

Frame your sentences and instructions in a simple manner, making them easily understandable for ChatGPT.

Be Concise

Choose shorter prompts and sentences. Break your instructions into smaller, coherent sentences for improved understanding.

Maintain Focus

Ensure that the prompt centers on a clearly defined topic to avoid the risk of producing overly generic output.

Consistency

Maintain a consistent tone and language during the conversation for a more coherent interaction.

Acting as…

The technique of having ChatGPT assume the identity of someone, or something has shown remarkable effectiveness. You can streamline the information you need from the model by instructing it to “act like” the desired person or system.

We’ve already seen the roleplay prompt example in the previous section, where ChatGPT acted as a detective.