Implement Trie (Prefix Tree)
Recognize the pattern
Brute force idea
The naive version of Implement Trie (Prefix Tree) sounds like this: Store strings in a list, linear scan for search and prefix — per operation. That direct path helps you understand the question, but it tends to treat every possibility as brand new instead of learning from earlier steps.
Better approach
The deeper shift in Implement Trie (Prefix Tree) is this: Trie: each node has up to 26 children (one per letter). Insert by walking/creating nodes per character. Search by walking nodes — if you reach the end with isEnd=true, the word exists. per operation where m = word length. Once you hold onto the right piece of information from moment to moment, the problem feels less like trial and error and more like following a shape that was there all along.
Key invariant
At the center of Implement Trie (Prefix Tree) is one steady idea: Each path from root to a marked node represents a stored word. Shared prefixes share nodes — 'apple' and 'app' share the first 3 nodes. This is what makes prefix operations instead of scanning all words. When you keep that truth intact, each local choice supports the larger solution instead of fighting it.
Watch out for
The trap in Implement Trie (Prefix Tree) usually looks like this: Forgetting to mark terminal nodes — without isEnd, you can't distinguish 'app' (a complete word) from the prefix of 'apple'. When the code becomes mechanical before the idea is clear, small edge cases start breaking the whole story.