Key principles for creating an effective prompt

When creating queries for AI tools, it is helpful to divide complex requirements into smaller, more understandable parts. This process is known as task decomposition.

Why is this important? 

Large, complex queries can often confuse AI systems, resulting in incomplete, superficial, or illogical answers.

By breaking your query into simpler steps, the model can better understand each part and gradually produce a more accurate and meaningful result.

How to do it?

• Instead of asking for everything at once, divide your query into smaller parts.

• First, request an explanation of the basic concepts.

• Next, ask for examples that clarify the context.

• Finally, request an application, comparison, or solution proposal.

Example:

Instead of a single query:

“Explain the theory, history, and applications of computer networks in education and create a lesson plan about it.”

It is better to ask a series of smaller, related questions:

• “Explain the basic concepts of computer networks.”
• “Give a brief history of the development of computer networks.”
• “How are computer networks used in education?”
• “Propose a lesson plan on computer networks for students.”

This approach increases the precision, clarity, and quality of the answers you receive and allows you to gradually build a more complex result based on accurate and verified parts.

Tip:

This technique is especially recommended in educational settings when using AI for lesson planning, creating teaching materials, or research analysis. It helps the model “think step by step” and reduces the possibility of error.