Tries Explained Simply (With Real Interview Problems)
A ground-up explanation of the trie (prefix tree) data structure aimed at coding interview preparation. Starts with an intuitive ASCII diagram showing how words share prefixes along root-to-node paths, then explains why tries beat hash sets for prefix queries with O(m) operations independent of dictionary size. Builds a complete, idiomatic Python Trie class with insert, search, and starts_with, analyzing time and space complexity along the way. Applies the structure to three classic interview problems — autocomplete (top-k suggestions per prefix), Word Search II on a 2D board with trie-guided backtracking and pruning, and Longest Word in Dictionary via BFS over complete words — each with a full worked solution.