Study & research

Practice Exam Question Set Generator

Generate multiple-choice or written practice questions with detailed explanations.

Prompt · 4 variables

The study material below covers the "{{exam scope}}" section for the upcoming {{academic subject}} exam. I need a set of practice questions to test my understanding before the test.

Please create 8 {{problem type}} questions based strictly on the provided study material. Focus on key conceptual differences and critical points of understanding, rather than trivial or minor factual trivia mentioned in passing.

Here is an example of the desired format, depth, and length. If the requested format is not multiple choice, keep the same structure (Question · Correct Answer · Explanation · Common Pitfalls): Question: Which of the following transport mechanisms occurs without expending energy? ① to ⑤ — Options should use terminology found in the text, and distractors must be plausibly challenging. Correct Answer: ③ Explanation: One line explaining why ③ is correct, and one line each explaining why ① and ⑤ are incorrect. Common Pitfalls: Confusing osmosis with active transport and assuming it requires ATP.

Distribute the 8 questions by difficulty: 3 Easy, 3 Medium, and 2 Hard. Label the difficulty level for each question. Place all correct answers and explanations together at the very end after all 8 questions.

Do not ask questions outside the provided study material. Exclude any ambiguous questions or items where the single correct answer cannot be strictly justified by the text. Do not claim or imply that these are official past exam questions.

Study Material: """ {{learning study material}} """

Copy, then paste here · ChatGPT and Claude open with the prompt filled in

Why it is written this way

This prompt is built from 6 blocks
Context +2 Task problem type Example Format Constraints Input +1 Material Work Material Output Material

Tap a block to see its paragraph

Context Material · What the AI needs to know

The study material below covers the "{{exam scope}}" section for the upcoming {{academic subject}} exam. I need a set of practice questions to test my understanding before the test.

See the Give context technique
  • Material What the AI needs to know ×3
  • Work What to do, and how ×1
  • Output What comes back, in what shape ×2

If you simply ask an AI to generate exam questions, it typically picks random nouns from the text and spits out shallow questions like "What is osmosis?" While this tests basic memorization, it misses the conceptual nuances tested on real exams. Explaining exact exam depth in words is difficult, but providing a reference example communicates standard expectations instantly.

Read the full explanation · 3 paragraphs

The Example paragraph achieves this. By showing a complete sample with the question, answer, explanation, and "Common Pitfalls," the AI captures the required depth, tone, and layout. Demanding plausible distractors directly inside the example ensures that incorrect options aren't obviously wrong, preventing the student from guessing the right answer effortlessly.

The Format paragraph specifies a 3-3-2 difficulty distribution (Easy, Medium, Hard) to ensure a balanced question set. Grouping answers at the very bottom prevents accidental spoilers while taking the test.

The Constraints paragraph preserves question reliability. Quizzing outside the provided material wastes study time, as do ambiguous questions with contested answers. Restricting the AI from claiming these are "real past exam questions" prevents halluncinated provenance and skewed study expectations. Enclosing study material in """ ensures any student notes like "Prof said this is on the exam!" are not parsed as AI instructions.

Unfamiliar terms? See Aha AI: few-shot, hallucination

Compared with a bad example

Missing from the bad prompt: Context · Example · Format · Constraints
Bad prompt 48 chars This prompt 1274 chars Context Task Example Format Constraints Input Context · missing Example · missing Format · missing Constraints · missing
Common bad example

Make an exam quiz based on this.

(paste textbook summary)

Produces a handful of trivial definition questions at an evenly low difficulty. In multiple-choice questions, the distractors are often absurdly obvious, allowing anyone to guess correctly without real understanding. Explanations usually appear right under each question, spoiling the answer before you attempt it.

Variations

1

Targeting Weak Concepts from Incorrect Answers

A targeted variation for drilling missed concepts instead of repeating the entire quiz. Use this in the same chat thread so the AI can reference the previous questions.

Show prompt

I will provide the question numbers I answered incorrectly from the {{academic subject}} "{{exam scope}}" practice set created earlier.

First, identify the core concept tested across these missed questions. Then, generate 5 new {{problem type}} questions approaching that same concept from different angles. Do not reuse sentences from earlier, and include a one-line "What you need to know to answer correctly" note under each question.

2

Grading Written Free-Response Answers

Free-response questions require clear rubrics to be effective practice. Withholding the model answers forces you to write out responses on your own first.

Show prompt

Please create 3 free-response written questions for {{academic subject}} based on the study material below. For each question, establish a grading rubric breaking down point allocations based on specific keywords or concepts included.

Present only the questions first and wait for my response. Once I submit my answers, grade them according to your rubric and indicate which sections of the study material I should review for any deducted points. Do not reveal the model answers beforehand.

Study Material: """ {{learning study material}} """

Model notes

After completing the questions, follow up with: "Give me 5 more questions testing the same concepts as the ones I missed in questions #3 and #7" to focus strictly on your weak areas.

Last updated 2026-09-02 · Found a mistake? Let us know