Classic BFS problem and I knew that going in, but I fumbled the visited set logic at first and was computing redundant paths.
Model the problem as a graph where each word is a node and edges connect words differing by one letter. Use BFS from the start word to find the shortest path to the end word, ensuring all intermediate words are in the given dictionary. Optimize by precomputing patterns (e.g., replacing each letter with '*') to quickly find neighbors.
Pro tip: Mention bidirectional BFS to significantly reduce search space, especially for large dictionaries, and discuss trade-offs with preprocessing time and memory. Also, clarify edge cases like when start and end words are the same or when no path exists.
Confirm the dictionary is provided, words are same length, and transformations must use valid intermediate words. Discuss cases like start == end, no path, or words not in dictionary.
Explain that words are nodes and edges exist between words differing by one letter. The goal is to find the shortest path from start to end.
Use BFS because it explores level by level, guaranteeing the shortest path in an unweighted graph. Describe how to track visited words to avoid cycles.
Precompute a mapping from patterns (e.g., 'h*t') to words to quickly find neighbors without comparing every pair. Alternatively, generate all possible one-letter variations and check against the dictionary.
Discuss time and space complexity: O(N * L^2) for preprocessing or O(N * 26 * L) for on-the-fly generation, where N is dictionary size and L is word length. Mention bidirectional BFS as an optimization.
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