Quick Revision Generator
Purpose: Create maximum-density revision notes for last-minute recall.
Format Specification
Structure
code
## [Topic] ### Core Facts - Fact 1 - Fact 2 ### Key Formulas - Formula: meaning ### Mnemonics - [Memory aid] ### Exam Traps - [Common mistake]
Compression Rules
| Rule | Example |
|---|---|
| One concept per bullet | TCP = reliable, connection-oriented |
| Formula + meaning | O(log n) = halving each step (binary search) |
| Contrast pairs | Stack = LIFO, Queue = FIFO |
| Acronyms | ACID = Atomicity, Consistency, Isolation, Durability |
| Pattern recognition | All ML: data → train → predict → evaluate |
Density Targets
| Topic Size | Target Lines |
|---|---|
| Single concept | 3-5 bullets |
| Unit/Chapter | 15-25 bullets |
| Entire subject | 50-80 bullets |
Must Include
- •Definitions - One-line precise
- •Formulas - With variable meanings
- •Differences - Between similar concepts
- •Examples - One canonical example per concept
- •Numbers - Key thresholds, limits, counts
- •Traps - Where marks are commonly lost
Must Avoid
- •Explanations (use teaching mode if needed)
- •Full sentences where phrases work
- •Redundant information
- •Examples that require context
- •Anything that needs re-reading
Output Example
code
## Quick Revision: Classification Algorithms ### Core - Decision Tree: splits on best attribute (Gini/Entropy) - Random Forest: bagging + feature randomness - KNN: majority vote of k nearest neighbors - SVM: finds maximum margin hyperplane - Naive Bayes: assumes feature independence ### Formulas - Entropy: H = -Σ p log₂(p) - Gini: 1 - Σ p² - Accuracy: (TP+TN) / Total ### Traps - KNN sensitive to k choice and scaling - Naive Bayes fails with correlated features - Decision Tree overfits without pruning