Focused on: Strings, DP, Backtracking

Start by asking whether the task is to optimize, count, decide, or list string results. Dynamic programming fits overlapping subproblems such as edit distance, word break, or longest common subsequence: define a state over prefixes or indices, state its meaning precisely, and derive transitions from the last operation or next choice. Backtracking fits enumeration such as restoring IP addresses or generating palindrome partitions, where every valid output may need to be produced; prune as soon as a prefix cannot lead to a valid result. Do not use backtracking for an optimization problem when memoization or DP can reuse repeated states, and do not force a DP table when output enumeration is inherently exponential. Test empty strings, repeated characters, no solution, multiple solutions, and recursion depth. Typical two-prefix DP is O(nm) time and space, while backtracking may be exponential in the number of choices, plus unavoidable output size.