In competitive programming, handling large inputs requires a combination of efficient algorithms, memory optimization, and mindful coding practices. Start by selecting an algorithm with optimal time complexity— for instance, using O(n log n) algorithms for sorting or searching in large datasets. Memory efficiency is equally important; avoid storing unnecessary data or creating large structures when not needed. For instance, when working with large matrices, consider using sparse data structures to store only essential elements. Avoid repetitive calculations by using techniques like prefix sums, dynamic programming, or memoization to reduce overhead. Testing your code on sample inputs close to the maximum constraints also helps gauge its efficiency and identify potential bottlenecks. With practice, handling large inputs becomes easier, enabling you to tackle more challenging competitive programming problems confidently.