Many marketers have adopted ChatGPT as their go-to tool for quick writing tasks or simple searches due to its ability to generate content rapidly, improve efficiency, and assist with brainstorming new ideas. While this approach works for basic requests where the output quality isn’t critical, achieving deeper, more precise results requires a structured approach to prompt design. By implementing a framework, we can standardize our prompts, test different structures, and refine phrasing to improve output consistency and quality.

To ensure effective prompts, I use a structured approach that helps guide the model’s responses and maintain consistency across different tasks. The model I prefer follows five key components: Role, Scenario, Task, Example, and Context. Below, I’ll break down each element and demonstrate how to craft a fully structured prompt. Finally, we’ll explore how to embed this framework into a custom GPT for even greater efficiency.

Role

The role sets the initial context for the conversation. Since ChatGPT can perform various functions, defining a specific role helps narrow the scope of its output.

For example, asking the model to act as an ad copywriter focuses its output on crafting concise, persuasive messaging, whereas instructing it to be a content marketer shifts it toward creating longer-form, educational content.

Some argue that models are becoming intelligent enough to infer context without an explicitly defined role. However, specifying a role still helps refine responses and provide structure. You can experiment with different role phrasings or even omit this step to assess its impact on your results.

Scenario

The scenario establishes the background for the request. Think of it as briefing a colleague who missed a meeting—what discussions took place, what decisions were made, and what limitations exist?

For example, if you’re asking ChatGPT to generate a social media post, you might specify:

• The campaign’s theme
• The post’s objective (e.g., driving traffic to a landing page, increasing engagement)
• The desired action from the reader
• The type of accompanying content (e.g., image, video, infographic)

Providing these details ensures the model understands the bigger picture and aligns its response accordingly.

Task

This is the direct instruction for the model. It should be clear and specific, such as “Write the post.”

This step ensures clarity in what you expect from the model. The task statement can also be naturally integrated into the scenario description to maintain flow.

Examples (Optional)

Examples help steer the model’s output toward a preferred style or format. These can take different forms:

• A final output that exemplifies the desired tone and structure
• A framework that outlines expected guidelines

For instance, if crafting a LinkedIn post, you might provide:

Hook + Read More format: Start with an engaging hook that highlights a pain point, followed by a compelling reason to read more.

If generating an article, you might specify:

Structure: Open with an introduction that sets the stage, outline the key sections, and conclude by reinforcing the main thesis.

Without an example, responses may vary significantly between prompts, so including one helps maintain consistency.

Context (Optional)

Context involves additional inputs that enhance the model’s understanding. This could be:

• A previously written article
• A link to a webpage
• A transcript from a meeting

Placing this information at the end of the prompt ensures clarity by keeping the core instructions upfront while allowing the model to process all relevant details before generating an output. This approach helps maintain logical organization and ensures that essential context is included without overwhelming the initial task statement.

Bringing It All Together

Here’s an example of a fully structured prompt using this framework. Let’s say we need ChatGPT to generate email copy for a newsletter:

Prompt:

[Role] Act as an email copywriter for a SaaS company specializing in [XYZ] industry, solving [ABC] problem.

[Scenario] We send a weekly newsletter promoting the latest articles, eBooks, and case studies from our website.

[Task] Write the first draft of the email copy.

[Example] Each newsletter item features a title that highlights a user pain point, a paragraph summarizing the problem and hinting at the outcome, and a strong CTA enticing the reader to click through.

[Context] Here’s the first article to include in this week’s newsletter: [Insert link]

This structured prompt ensures ChatGPT delivers a tailored, high-quality response while maintaining consistency across multiple uses.

Creating a GPT to Automate Prompting

Once you refine a prompt and consistently get strong outputs, you can automate the process by creating a custom GPT.

Most of the structured prompt elements—Role, Scenario, and Task—can be integrated into the GPT’s instructions. You can also upload example outputs to guide the model’s style and format. This setup reduces the manual work needed for each request, allowing you to input only the context (e.g., a link to an article) while the GPT handles the rest.

By leveraging this framework and integrating it into a custom GPT, marketers can streamline their workflows, improve efficiency, and maintain a high level of consistency in their content. This structured approach ensures clear, high-quality outputs while reducing the time spent refining responses.