Apparently this comes up a lot in OpenAI screens.
Start by clarifying the problem constraints (e.g., grid size, word list, placement rules) and then propose a backtracking algorithm that places words one by one, checking validity at each step. Optimize by ordering words by length or constraints and using pruning techniques like early conflict detection.
Pro tip: Demonstrate awareness of real-world applications like crossword puzzle generation and constraint satisfaction problems, and mention how you would test edge cases such as overlapping words and impossible placements.
Ask questions to understand the exact constraints: grid dimensions, word list, allowed directions, and whether words can overlap. Confirm if all words must be placed or if some can be omitted.
Select a backtracking approach where you recursively try placing each word in all possible positions and orientations. Consider using a trie for efficient prefix matching if the word list is large.
During backtracking, prune branches early by checking if the current grid state can still accommodate remaining words. Use heuristics like placing longer words first or words with fewer placement options.
Discuss time and space complexity, noting that worst-case is exponential but pruning and constraints often make it feasible. Mention potential optimizations like memoization or constraint propagation.
Outline test cases: simple grids, overlapping words, impossible configurations, and large inputs. Verify correctness and performance, and consider edge cases like empty word list or 1x1 grid.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.