Writing effective prompts for AI question generation

The quality of AI-generated questions in LearningLemur depends directly on the prompt you provide. A well-written prompt helps the system generate questions that match your intended topic, difficulty level, and structure.

This article explains how to write effective prompts and how to improve the quality of generated questions.

What It Is

A prompt is a textual description that tells the AI what kind of questions to generate. It defines:

• The mathematical topic.
• The type of task.
• The expected level of difficulty.
• Any specific constraints.

The AI uses this information to generate question drafts.

Why It Matters

The AI does not infer context beyond what you explicitly provide. A vague prompt may produce:

• Unclear questions.
• Inconsistent difficulty.
• Missing requirements.

A precise prompt leads to:

• More accurate questions.
• Better alignment with your goals.
• Less manual editing.

How It Works

Principle 1: Be specific about the topic

Clearly describe the mathematical concept.

• Good: Solve linear equations with one variable
• Too vague: Math problems

Principle 2: Specify the task type

Indicate what students are expected to do.

• Good: Create problems where students must simplify fractions
• Ambiguous: Fractions

Principle 3: Define the difficulty level

Mention the target level or constraints.

• Good: For middle school students with integer results
• Missing context: Solve equations

Principle 4: Add constraints when needed

Include any requirements that affect the structure of the question.

• Good:
  • Require answers in simplified form
  • Use numbers between 1 and 10
  • Include units in the answer

Principle 5: Combine multiple instructions

A strong prompt combines topic, task, and constraints.

• Example: Create 3 open-answer questions where students solve linear equations with one variable. Use integer coefficients and require simplified answers.

Key Rules or Behaviours

• The AI relies entirely on the prompt content.
• Missing information leads to less predictable results.
• More detailed prompts generally produce better outputs.
• Overly complex prompts may produce inconsistent results.

Examples

Example 1

Example 2

Example 3

Common Misunderstandings

Misconception: The AI will infer the intended difficulty.
Clarification: Prompts guide generation, but manual review is always required.

Misconception: The AI will generate complete, ready-to-use questions.
Clarification: Prompts should be clear and structured, not overly verbose.

Misconception: Adding more text always improves results.
Clarification: Prompts should be clear and structured, not overly verbose.

Related Concepts

• How AI question generation works in LearningLemur
• Creating questions in LearningLemur
• Understanding Question Types in LearningLemur