Clarify the problem constraints and edge cases first, then propose an efficient algorithm that handles overlapping matches and case-insensitive replacement while preserving original casing. Discuss trade-offs between different approaches (e.g., regex vs. trie) and consider scalability for large inputs.
Pro tip: Mention that you would use a case-insensitive regex with word boundaries or a trie for efficient multi-pattern matching, and highlight the importance of preserving original casing by capturing the matched text. Also, discuss how to handle overlapping matches by prioritizing longer substrings or leftmost-longest matching.
Ask about input size, whether substrings can overlap, if replacements should be recursive, and how to handle multiple tags for the same substring. Confirm that the output should preserve the original casing of the matched text.
Decide between a simple iterative approach (for small inputs) or an efficient multi-pattern matching algorithm like Aho-Corasick or a trie. Consider using regex with case-insensitive flags for simplicity, but be aware of performance implications.
Determine how to resolve overlaps: typically leftmost-longest match wins. Sort substrings by length descending to ensure longer matches take precedence, or use a trie to find the longest match at each position.
Iterate through the string, find matches case-insensitively, and replace each with [tag]{matchedText} where matchedText is the exact substring from the original string. Use a StringBuilder for efficiency.
Test with edge cases: empty string, no matches, overlapping substrings, different cases. Discuss time/space complexity and potential optimizations like precompiling regex or using a trie for O(n) matching.
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