The Perfect Prompt Formula for ChatGPT: The Ultimate Guide

Marketing Explained| 00:07:16|Mar 25, 2026

Chapters7

The chapter emphasizes that prompt quality is an art, not a command, and that well-crafted prompts improve AI responses across tools, with practical guidance for digital marketing. It also promises methods applicable to various AI systems beyond chat GBT.

Craft precise, structured prompts using proven frameworks to maximize AI responses and avoid hallucinations in marketing workflows.

Summary

Marketing Explained’s video presents a practical blueprint for writing prompts that get better AI results. The host argues that prompts matter as much as the models themselves, and shows how clearer instructions reduce generic outputs. The piece highlights that prompts work across tools like Perplexity, Claude, Gemini, DeepSeek, and Microsoft Copilot, not just ChatGPT. It then explains why AI can’t read minds and how structured questions improve accuracy and usefulness. A concrete example contrasts a vague prompt with a specific one referencing Gartner and HubSpot, illustrating higher-quality insights. Three prompt frameworks—Aspect, TEDA, and CLO—are detailed with actionable elements such as objectives, context, constraints, and templates. Finally, the video introduces CyberClick’s eco methodology to organize prompts into six elements and covers privacy and security best practices when handling data with AI systems.

Key Takeaways

Use a specific, data-backed prompt like: 'Give me an analysis of digital marketing trends for 2025 based on Gartner and HubSpot reports prioritizing content strategies and sales automation.'
Three frameworks are outlined: Aspect (Action, Steps, Persona, Example, Context, Constraints, Template), TEDA (Task, Expected Action, Model Role, Output Example, Additional Clarifications), and CLO (Context, Instructions, Conditions, Limits, Desired Output).
The eco methodology from CyberClick structures prompts into six elements: objective (final task, steps), context (brand, value prop, goals, persona), and expectations (output format, style, templates).
Prompts should include an explicit refusal policy like 'If you can't find the information, don't fill in this data' to curb fabrication.
Security and privacy are emphasized: anonymize data, avoid sharing sensitive information, and consider whether the AI uses data for training.
Acknowledge the AI’s limitations—you must provide clear questions and avoid assumptions or stereotypes to prevent incorrect responses.

Who Is This For?

Essential viewing for digital marketers and AI practitioners who want reliable, branded prompts. Great for teams building a reusable prompt library and for anyone adopting prompt engineering to improve decision-making with AI tools.

Notable Quotes

"AI doesn't read our minds."

—Sets up the core reason prompts need to be clear and structured.

"If you can't find the information, don't fill in this data."

—Emphasizes cautious data handling and accuracy in prompts.

"A prompt engineering framework is an organized structure to formulate our instructions to the AI."

—Introduces the concept of frameworks as a backbone for better prompts.

"Let's move on to the next important point."

—Signal of transition to new.prompt sections, keeping viewers focused.

"Remember, if you want quality responses, start by improving the quality of your prompts."

—Wraps up the video's core recommendation for viewers.

Questions This Video Answers

How do I write a high-quality prompt for ChatGPT and other AI tools?
What are the best prompt frameworks for marketing tasks?
What is the eco methodology and how can it improve prompt quality in digital marketing?
How can I prevent AI hallucinations when requesting data-driven insights?
Which AI tools are covered and how do prompts translate across them?

AI prompt engineeringPrompt frameworksAspect frameworkTEDA frameworkCLO frameworkCyberClick eco methodologyData privacy in AIDigital marketing AI toolsHubSpotGartner

Full Transcript

If you use chatgbt but you're not satisfied with the responses, maybe the problem lies in your prompts. Because let's face it, repeating the same thing over and over, but in all caps, doesn't help. Writing good prompts is almost an art. It's not just about giving a command, but about constructing a clear, detailed, and well- formulated instruction. A good prompt can make the difference between a generic response and one that truly helps you make better decisions. And don't worry because the recommendations we're going to share with you in this video will be useful not only for chat GBT but for any other AI system like Perplexity, Claude, Gemini, DeepSeek, Microsoft Copilot, or whichever one you use more frequently. So let's look at why writing good prompts is important, how to avoid common mistakes, and the best methodologies so you can apply them to your digital marketing strategies. Let's get started. AI doesn't read our minds. If we want precise answers, we need to ask clear, structured, and detailed questions. A good prompt not only improves the quality of the response, but also avoids what is known as hallucinations in AI, which are incorrect or madeup responses. For example, if we ask an AI, what are the digital marketing trends for this year? It might give us a very generic answer based on past data. But if we say, "Give me an analysis of digital marketing trends for 2025 based on Gartner and HubSpot reports prioritizing content strategies and sales automation," the quality of the response will significantly improve. That's why it's important that you keep in mind these tips to avoid AI hallucinations and the generation of incorrect information. Include phrases like, "If you can't find the information, don't fill in this data." If you need more information, ask me whatever questions you deem necessary. Do not make assumptions in your response or do not base your response on stereotypes or unfounded value judgments. These small details can make the difference between receiving a useful response or a completely incorrect one. Let's move on to the next important point. What is a framework and why it can help you to improve the quality of our prompts? There are frameworks. A prompt engineering framework is an organized structure to formulate our instructions to the AI. By following a framework, we can ensure that the prompt contains all the key elements to obtain precise, specific, and highquality responses. The basic structure of any framework consists of an objective, what task we want the AI to perform, a context, additional information that helps the AI better understand our request, and some expectations, how we want to receive the response, be it format, style, level of detail, etc. However, many companies and thought leaders in the AI world develop their own frameworks and have a personal prompt library. This helps them adapt the general structure exactly to the specific prompt they send to the model. That's why several framework structures have become popular. Let's now look at three structures you can use to optimize your prompts. One, aspect. This framework is ideal for detailed and structured prompts. It's divided in action. Define what we want the AI to do. Steps. Divide the task into concrete steps. Persona, define the role of the AI. Expert in marketing, copywriter, etc. Example, provide clear examples to guide the AI. Context. Provide additional relevant information. Constraints. Define limits for the source of information or the length of the text. And template. Include a template so that the answer has the right format. Two. Teda framework. This one is simpler and quicker to apply. It works for task. Clearly state the goal or action the model should perform. Expected action. Specify what the model is expected to do in response to your request. That is the order in which you wanted to perform the task. For example, analyze the content of this webinar first and then write an email to invite my database highlighting the points that might be most interesting to them. Model role. Define the role the model should assume to tackle the task. For example, you're an expert in email marketing. Output example. Provide an example of what you consider a suitable response. For example, upload a few emails you've written in the past so the model can replicate your tone and style. Additional clarifications. Include details that may help the model better understand the task. Perhaps you want to give it a maximum number of characters the email should have. Three. Clo framework. This framework is intended for task with many restrictions and rules as it focuses more on defining conditions and limits in our prompt. Its acronym stands for context. Provide the necessary information for the model to understand the situation. Instructions. Clearly and precisely explain what you expect the model to do. Conditions. Establish the rules or requirements that must be met during the task. Limits. Define the limits that must be considered to accomplish the task. Desired output. Explain what the final result should be like and what characteristics it should have. To apply frameworks effectively in digital marketing at CyberClick, we propose the eco methodology which allows you to structure the three key blocks of a prompt, expectations, context, and objective into six elements that will help you draft your message in greater detail. Within the objective block, we find final task. Here we explain what we want to achieve, steps to achieve it. We divide the task into clear steps. Within the context block, we find business context. Add information about the brand, the value proposition, and the goals. Ideal customer define the buyer persona and their behavior in sufficient detail within the expectations block or desired output format. We expect the AI model to return, we find, best practices or format. Give instructions on style, tone, or technical requirements. Examples or templates. Add previous references to maintain consistency and responses. With this structure, you can be sure the results you get from AI will improve significantly by being more specific in your request. Let's move on now to a very important topic, privacy and security and prompts. When interacting with AI models, we must consider data privacy. That's why it's essential to avoid sharing sensitive information about our company and to use some best practices such as anonymize data. Avoid using real names of our company or employees and never share sensitive customer data unless we have permission to do so. Investigate what the AI model will do with the data. That is whether it uses it to train the model, shares it with third parties, and other possible scenarios. Whenever possible, disable the use of data for training on AI platforms like chat GPT. Implement security protocols such as data encryption and also have an internal road map to know which tools we use to centralize professional versus personal request. These are just some of the tips you can consider, but if you want to delve deeper into this topic, leave us a comment and we'll take it into account to make a video going forward. In the video description, we've included useful data privacy resources based on your region. Remember, if you want quality responses, start by improving the quality of your prompts. Use frameworks, follow methodologies, and customize according to context. And have your own reference prompt library. If you want to go further, you can now watch this other video about creating your own custom GPT. If you like this video, don't hesitate to subscribe to our channel to stay uptodate on all our content about digital marketing and artificial intelligence. See you in the next video.