📦 Other & Niche
Analyze Previous Year Question Papers
Example outputs
Here is what ChatGPT, Claude, and Gemini typically return for this prompt:
Analysis of the 5 uploaded papers (2021–2025), mapped to your syllabus: Chapter 3 (Electrochemistry) appeared in every paper — 100% frequency, average weight 12 marks, usually as a numerical on Nernst equation. Chapter 7 (Coordination Compounds) appeared 4/5 times, always as a 3-mark theory question on isomerism. Low-yield: Chapter 11 appeared once in five years. Priority order for revision: Ch 3 > Ch 7 > Ch 1 > Ch 5. Predicted high-probability questions for 2026 attached below.
I've reviewed the question papers against the syllabus you provided. Topic frequency table: Thermodynamics — 5/5 years (avg. 14 marks, trending toward application-based problems since 2023). Rotational Motion — 4/5 years. Wave Optics — 3/5 years but rising in difficulty. Notable pattern: the examiner recycles numerical templates on a 3-year cycle; the 2023 projectile format is statistically due to reappear. One honest caveat: past frequency is a strong but not certain predictor — cover the full syllabus at least once before weighting your revision this way.
📊 Repeated-topic report: from 8 papers analyzed, 6 topics account for 61% of total marks. Top three: Calculus – Definite Integration (appears every year, 2 questions avg.), Probability (7/8 years), Matrices & Determinants (7/8 years, always Section B). Chapter-wise heatmap and a 30-day revision schedule weighted by marks-per-topic are generated below. Weakest ROI: Mathematical Reasoning — 1 mark average, skip-safe if time is short.
How to use this prompt
Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.
Which AI model works best
ChatGPT, Claude, and Gemini all produce useful results for this type of prompt. Claude is usually the most nuanced, ChatGPT the fastest, and Gemini the best when visual input or Google Workspace data is involved.
How to customize this prompt
Adapt the prompt to your specific use case. Replace placeholders (usually in brackets or caps) with your own context. The more detail you provide, the more precise the response.
Common use cases
- Use directly in ChatGPT, Claude, or Gemini
- Adapt to your specific project or industry
- Use as a starting point for your own custom prompt
- Compare across models to find the best fit for your case
- Share with your team as a standard workflow
Variations
Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).
Frequently asked questions
What is the Analyze Previous Year Question Papers prompt used for?
An Educational Content Analyst. You will analyze uploaded previous year question papers to identify important… Free AI prompt for ChatGPT, Claude & Gemini.
Which AI model works best for the Analyze Previous Year Question Papers prompt?
ChatGPT, Claude, and Gemini all produce useful results for this type of prompt. Claude is usually the most nuanced, ChatGPT the fastest, and Gemini the best when visual input or Google Workspace data is involved.
How do I customize the Analyze Previous Year Question Papers prompt?
Adapt the prompt to your specific use case. Replace placeholders (usually in brackets or caps) with your own context. The more detail you provide, the more precise the response.