My first instinct was to precompute all abbreviations at k=1 and then resolve conflicts in a second pass, but I kept second-guessing whether I needed to re-check global uniqueness after each round of conflict resolution.
Start by clarifying the abbreviation rules and edge cases, then propose a solution using a hash map to group words by their initial abbreviations. For conflicts, iteratively increase the prefix length for those words until all abbreviations are unique, and finally compare each abbreviation's length to the original word, returning the shorter one.
Pro tip: Demonstrate awareness of trade-offs: for example, mention that while the iterative approach is simple, it could be optimized by sorting words or using a trie to reduce redundant comparisons, especially for large datasets.
Ask questions to confirm the abbreviation format, handling of short words (e.g., length ≤ 2), and whether the output should preserve the original order.
Outline a plan: compute initial abbreviations for all words, group words by abbreviation, and for each group with size > 1, increment the prefix length for those words until unique.
Use a hash map to track abbreviations and a list to store words per abbreviation. For conflicts, update prefix lengths and recompute abbreviations, ensuring no infinite loops.
For each word, compare the length of its unique abbreviation with the original word; if the abbreviation is not shorter, return the original word.
Discuss time and space complexity, and walk through test cases like words with common prefixes, single-character words, and all words conflicting.
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