Backtracking is inherently slow because it often has an exponential time complexity, especially when the problem has many possible combinations or permutations. For large inputs, a brute-force backtracking approach will usually time out. To optimize backtracking solutions, consider using pruning techniques to cut off unnecessary branches in the search tree early. For example, if you're generating permutations or combinations, skip cases that are clearly invalid early in the recursion process. You can also use memoization to avoid recalculating results for the same states. By pruning and avoiding redundant work, you can make backtracking more efficient and handle larger inputs.