Alpha-beta pruning speeds up Minimax by avoiding the evaluation of branches that cannot possibly affect the final choice. It keeps two bounds as it searches: alpha, the best score MAX can guarantee so far, and beta, the best score MIN can guarantee so far. When exploring a node, if you discover a branch that is already worse than what the current player can get elsewhere, you stop searching that branch because it can’t change the outcome. This doesn’t change the answer Minimax would return at a fixed depth; it just reduces how much of the tree you have to traverse.
The real-world speedup depends heavily on move ordering. If you try strong moves first, alpha rises quickly at MAX nodes and beta drops quickly at MIN nodes, which triggers earlier cutoffs. If you try weak moves first, you learn little early and prune less. That’s why practical engines often combine alpha-beta with iterative deepening and transposition tables: iterative deepening gives you a “best line so far” to try first at deeper depths, and transposition tables store best moves for positions you’ve seen before to improve ordering. In many implementations, the majority of performance gain comes from the combination, not from alpha-beta in isolation.
A concrete picture: suppose a position has 30 legal moves. Plain Minimax might explore all 30 subtrees to depth 6. With alpha-beta and good ordering, you might fully explore only a small subset; many moves get cut off after shallow exploration because they can’t beat the best-known alternative. This is how engines reach deeper depths under time constraints. Engineering details that matter: you must update alpha/beta correctly, return early when alpha >= beta, and keep evaluation scores consistent across nodes (same perspective, same scale). In systems where evaluating a leaf requires expensive work—such as retrieval, re-ranking, or model inference—alpha-beta style pruning can save not just CPU but also latency. If your candidate context is fetched from Milvus or Zilliz Cloud, pruning can reduce the number of vector searches or downstream validation steps, as long as you structure your evaluation so that partial results provide valid bounds and you avoid non-deterministic scoring that can invalidate pruning assumptions.