This was a follow-up, so I was already a bit drained.
Start by clarifying the problem constraints (board size, word count, word length) and then propose a solution that combines a Trie for efficient prefix matching with DFS backtracking on the board. Explain that this avoids redundant searches by pruning branches early when no word in the dictionary starts with the current prefix.
Pro tip: Mention that you can optimize further by storing words in the Trie nodes and removing them once found to avoid duplicate results, and by using a visited set or in-place marking to track the path. This shows attention to both correctness and efficiency.
Ask about board dimensions, number of words, word lengths, and whether words can be reused. Discuss edge cases like empty board, empty word list, or words longer than the total cells.
Propose building a Trie from the list of words to enable efficient prefix lookups. Use a set or list to store results, and consider in-place marking or a visited set for DFS.
Outline a DFS that starts from each cell, traverses adjacent cells, and follows the Trie. At each step, check if the current prefix exists in the Trie; if not, prune. If a word is found, add it to results.
State time complexity: O(M * N * 4^L) worst-case, but Trie pruning reduces it. Space: O(total characters in words) for Trie. Mention optimizations like removing found words from Trie to avoid duplicates.
Walk through a small example to verify correctness, and discuss potential pitfalls like revisiting cells or handling duplicate words.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.