LLM-Wiki Revision

Synthesise your course into an autonomously maintained wiki

Ask specific questions and get directly relevant responses to your course's teaching materials. No mark scheme? No problem.

Why WikiRevision?

Grows As You Do

WikiRevision maintains and builds an inter-linked Wikipedia for your course. It's not a static chat; it's an evolving knowledge base to aid your learning.

Save Time & Tokens

When solely using an AI model, uploading your documents every time wastes resources. We store your context efficiently, so you never have to re-upload.

Laser-Focused Terminology

General AI gives general answers. WikiRevision ensures that the specific, exact terminology of your course is strictly referenced in every response.

Intelligent Marking

Mark past papers with detailed course knowledge. Get targeted feedback and grading that actually aligns with what your professors are teaching.

Example Query and Response

(The links to relevant wiki pages are shown in blue)

What is the difference between Prim's and Kruskal's algorithm?

According to the wiki, the primary differences between Prim's and Kruskal's algorithms for finding a minimum-spanning-tree (MST) are their underlying strategies and performance characteristics:

1Fundamental Strategy

  • Prim's Algorithm is vertex-centric. It grows a single tree from an arbitrary starting vertex, adding the least-cost edge that connects a vertex in the tree to one outside the tree at each step (L9-minimum-spanning-tree-prim).
  • Kruskal's Algorithm is edge-centric. It builds a "forest" by sorting all edges by weight and adding the shortest edge that does not form a cycle, eventually merging separate components into a single tree (L10-minimum-spanning-tree-kruskal).

2Efficiency and Graph Density

The choice between the two often depends on the graph's density (mst-algorithms):

  • Prim's is generally better for dense graphs (where E ≈ V²). Its basic complexity is O(V²), though it can be improved to O(E + V log V) using Fibonacci Heaps.
  • Kruskal's is generally better for sparse graphs (where E is closer to V). Its complexity is O(E log E) or O(E log V), which is dominated by the initial sorting of edges.

3Implementation and Data Structures

  • Prim's relies on a Priority Queue (min-heap) to efficiently find the next vertex to add to the growing tree (mst-algorithms).
  • Kruskal's requires Disjoint Set Union (Union-Find) with path compression and union by rank to efficiently check for cycles as edges are added (L10-minimum-spanning-tree-kruskal, mst-algorithms).

4Specialized Applications

  • Kruskal's MST can be used as a heuristic for the Travelling Salesman Problem (TSP); a tour created by traversing the MST is guaranteed to be less than twice the length of the optimal TSP tour (L10-minimum-spanning-tree-kruskal).