Predict Exam Topics with Gemini: A 9-Step Workflow Using Past Papers
aitrendz_xyz · x · 2026-08-11
This thread shares a practical workflow for using Google Gemini to predict exam topics:
- Collect & Organize: Gather the last 5 years of exam papers (5 is the sweet spot before syllabus changes). Name files by year to help the AI track topic shifts.
- Clean Data: Convert PDFs/scans to clean text, fix OCR errors, and remove headers/footers. Split into individual questions. Clean text sharpens AI analysis.
- Prompting: Ask the model for short outputs (6-8 bullets, 6-10 words each) so insights aren't buried. The prompt should request the top 10 recurring topics, top 5 likely topics for the next exam (with confidence %), common question types, and topics appearing every year.
- Verify: Check the top 3 predicted topics against the latest unused paper to validate data reliability.
Labeling questions by topic/chapter (optional) turns rough guesses into sharp predictions.
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