The Myth of the Magic Prompt: Why AI Results Depend on Context

In an earlier article titled “Using AI Effectively,” I discussed a central idea: differences in the results we get from AI tools often come down not to the model's capabilities, but to how we use it and how clearly we think before giving it an instruction.

A recent conversation with a friend offered a practical example. He wanted to update his CV and identify its strengths and weaknesses so he could improve it. His question was direct:

“What is the best AI model for this, and what is the best prompt I can write to get the ideal result?”

My response focused on correcting that assumption. There is no model that is universally “the best,” and no ready-made “magic prompt” that guarantees success.

What matters more than the tool is the context we give it. Before asking a model to assess a CV, we need to define our goals precisely. When the problem is well specified, the model is more likely to produce a response that is relevant to the actual need.

I asked him:

• Who is the intended audience?
• Which market needs does your experience address?
• What level of company are you targeting?
• Which geographic market are you in?

The deeper the context and the more precise the model's understanding of the challenge, the more accurate and relevant its output will be.

To achieve that level of interaction, I suggested using models that support systematic reasoning, often called thinking models, because they can analyse the information and produce answers with greater depth and creativity.

I also noted that voice conversation can be an excellent way to explain these details and contextual factors smoothly and clearly, giving the model a complete picture of the intended goal.

Most importantly, effective AI use is not a one-step process. It is an interactive dialogue that begins with understanding the problem. You provide context, receive an initial result and treat it as an intelligent draft that remains open to review.

The user then evaluates that draft and gives the model new feedback to refine the answer. This exchange continues until the result is a mature report that offers a genuine, practical path to improving the CV.

Ultimately, this practical example reinforces the earlier point: AI does not replace people. The most effective relationship is complementary. The tools are available to everyone, but the real difference comes from clear vision, sound thinking, the ability to ask the right questions and a critical approach to reviewing the answers.

About the author: Mohamed Nasr Eldin is a senior civil and structural engineer and engineering manager with more than 20 years of experience in construction. He also works as an AI integration engineer, applying AI to engineering workflows to improve productivity and support decision-making. He is interested in making knowledge accessible and sharing it.