Knew this one from practice but the bitmask part took me a minute to articulate out loud.
Use backtracking to explore all subsequences, pruning branches when character conflicts occur. Alternatively, use bitmask DP to represent character sets and track maximum length for each mask. Discuss trade-offs between exponential backtracking and DP with state compression.
Pro tip: Clarify constraints first (e.g., string length, character set) to choose the right approach; mention that bitmask DP is efficient for small alphabets but backtracking with pruning works well for sparse conflicts.
Ask about input size, character set (e.g., lowercase letters), and whether empty strings are allowed. This determines the optimal approach.
Use a bitmask to represent the set of characters used so far, or a frequency array. For DP, state is the mask; for backtracking, pass the current mask and length.
For each string, decide to include it or skip it. When including, check if its characters conflict with the current mask; if not, update mask and length.
Prune branches where the maximum possible additional length cannot beat the current best. Use memoization (e.g., map from mask to max length) to avoid redundant work.
Discuss time complexity (O(2^n * n) for backtracking, O(2^26 * n) for DP) and space. Suggest optimizations like pre-filtering strings with duplicate characters.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.