Master one admissions test question type with a strategy and drills
How a tricky question type works, a step-by-step method, its common traps, and graded practice items with explanations.
The prompt
Teach me to master one question type on my graduate admissions test.
Test: [e.g. "GMAT", "GRE", "LSAT"]
Question type: [e.g. "GMAT data sufficiency", "GRE text completion", "LSAT flaw questions"]
My current accuracy and timing on it: [PASTE FROM STEP 2, or estimate]
Mistakes I keep making: [e.g. "I solve fully instead of checking sufficiency", "I pick answers that are true but don't address the flaw"]
Return:
1. How this question type works: what it is really testing and how the answer choices are built.
2. A step-by-step method I can apply every time, short enough to memorize.
3. The common traps and wrong-answer patterns, with how to spot each.
4. Time targets for this type and when to guess and move on.
5. 8 practice items that get harder as they go, all questions first, then an answer key explaining why each answer is right and why the tempting wrong answers are wrong.
6. How to review my misses so the same mistake doesn't repeat.
Rules:
- Test formats change. If you describe format details (number of questions, timing, section structure), mark them [CONFIRM against the official test site].
- Say clearly that AI-written items may not match the real test's difficulty or style exactly, and recommend official practice questions for my main drilling.
This is step 3 of the "Prepare for a graduate admissions test" pack. It works on its own, and can use the priorities from the step 2 diagnosis.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Learning & Teaching prompts
What to fill in
- e.g. "GMAT", "GRE", "LSAT"
- PASTE FROM STEP 2, or estimate
- CONFIRM against the official test site
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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