When implementing algorithms, prioritize computational efficiency by selecting appropriate data structures and avoiding unnecessarily complex approaches. Consider the time complexity of your solution and whether built-in functions or better data structures could improve performance. Key principles: - Use built-in functions that provide early exit behavior instead of manual exception handling - Choose appropriate data structures (sets for deduplication, hash tables for lookups) - Avoid expensive operations like JSON serialization for equality comparisons - Be mindful of quadratic complexity, especially when processing large datasets Examples of improvements: Instead of manual exception handling for early exit: ```ocaml (* Avoid *) try List.fold_left (fun _ x -> if condition x then raise (Found x)) () lst with Found x -> Some x (* Prefer *) List.find_opt condition lst ``` Instead of manual folding for set operations: ```ocaml (* Avoid *) List.fold_left (fun acc m -> StringSet.add m.path acc) StringSet.empty matches (* Prefer *) matches |> List.map (fun m -> m.path) |> Set_.of_list ``` Instead of expensive equality comparisons: ```ocaml (* Avoid *) let a_json = a |> to_json |> to_string in let b_json = b |> to_json |> to_string in String.equal a_json b_json (* Prefer *) Common2.on String.equal extract_key a b ``` When dealing with potentially large datasets, document the time complexity and consider whether O(N²) algorithms need optimization. For stack-intensive operations, prefer iterative approaches or tail-recursive functions to avoid stack overflow issues.