Perhaps one of the biggest challenges for current AI users (both developers and content creators) is formulating prompts that really work. Specifying the how, when, context, format, tone… everything is important. And a lot. What is the best strategy to start with? Be explicit or leave room for creativity? How many details should be given? How should it be structured? Is it possible to simplify a task that still has a lot of room for improvement?
Why is it so complicated?
Finding a prompt that truly unlocks the full potential of AI is difficult. The key, in reality, lies in the precision with which we generate the instruction. To perform at its best, AI needs to know things like the user’s context, who their audience is and what they expect, the output format (bullet points, narrative, JSON, etc.), and even examples or step-by-step thought processes.
Bringing all these features together is neither simple nor trivial, and writing it manually takes time and effort.
There is also the mistake of treating the model as a brilliant employee “but one who has amnesia.” Without providing structure and context, the result is likely to be out of focus. It is not uncommon to improvise and then polish, but this requires knowledge and practice.
Imagine a tool that makes it easy for you…
With all the difficulties we have seen so far, it is not surprising to imagine a tool that could facilitate all this from scratch. A solution that, with a simple interface, would allow you to explain in natural language what you want to achieve.
This way, you could, for example, indicate that you want to write a technical summary for executives in five bullet points, and… as if by magic, the tool would return a structured prompt ready to copy.
In this way, we would use AI to generate an instruction that extracts the maximum possible value from another AI. Without needing to specify it in the original instruction, the generated prompt would ask the model to “think before writing” by structuring the prompt in stages.
It would automatically add clear labels such as objective, format, or audience, acting as a clear and readable internal guide.
It would include examples or editable variables to adapt the style, and could even apply techniques such as generating a chain of thought or assigning a role to maximize accuracy.
In summary, I would enrich the initial request with as much context as possible, including all the details, so that the final result would be as specific and useful as possible.
Simplicity… with great potential
For such a tool to work, it would need to be easy to use. Complex interfaces and the need for prior technical knowledge would have to be left behind.
It should be as easy as selecting a department to which the request would be assigned (e.g., human resources). Add a specific objective, allow additional context to be included, and even define an output format (text, table, JSON, etc.) or a tone and style.
The usefulness of a tool like this would be immediate. Even professionals who are not familiar with prompt engineering or entire teams that depend on rapid interactions with AI could save time, encouraging the quality and accuracy of prompts.
For novice users, it would be a clear and educational aid that would allow them to delve into prompt engineering without a learning curve.
For advanced users, it would become a catalyst that would accelerate experimentation and optimization, allowing them to focus on adjusting the content without worrying about the structure of the prompt.
Teams would benefit from standardizing the prompts used in projects, with shareable, variable templates that are easy to iterate.
The true potential of a well-designed prompt
Achieving a well-designed prompt, reaching that “backbone” that guides the system toward a truly useful solution, specifying the task, preparing the logical structure, and being clean and editable, is a goal that would not only solve “blank page panic.”
Such a tool would democratize access to more accurate and professional interactions with AI. Its real usefulness would not lie in requesting knowledge, but in providing it through built-in technology. A small idea with enormous potential that would transform how we interact with these intelligent tools that are revolutionizing the way we work.
