Alpha beta pruning is a technique used in game tree search algorithms to improve their efficiency. It is a way of trimming the search tree by pruning branches that cannot possibly affect the final outcome of the search. This is done by maintaining two values, alpha and beta, which represent the minimum and maximum scores that the current player is assured of, based on the current state of the game.

For example, consider a game of tic-tac-toe where one player is "X" and the other is "O". The game tree for tic-tac-toe is relatively small, as there are only a few possible moves at each turn. However, the game tree for a more complex game such as chess or Go is much larger and more difficult to search through.

In the alpha beta pruning algorithm, the search tree is traversed in a depth-first manner, and at each node, the algorithm compares the alpha and beta values to determine whether the current branch can be pruned. If the alpha value is greater than or equal to the beta value, then the current branch can be safely pruned because the player whose turn it is cannot possibly achieve a score better than the alpha value.

Let's consider an example of alpha beta pruning in a tic-tac-toe game. Suppose that it is X's turn, and X has the following possible moves:

- X can place an "X" in the top left corner of the board, resulting in a score of 1 for X.
- X can place an "X" in the top middle of the board, resulting in a score of 1 for X.
- X can place an "X" in the top right corner of the board, resulting in a score of 1 for X.

At this point, the alpha value is 1, because X has already achieved a score of 1 by placing an "X" in the top left corner. The beta value is -1, because O has not yet had a chance to play and therefore has not achieved a score.

Suppose that O now has the following possible moves:

- O can place an "O" in the middle left of the board, resulting in a score of -1 for O.
- O can place an "O" in the middle middle of the board, resulting in a score of -1 for O.
- O can place an "O" in the middle right of the board, resulting in a score of -1 for O.

At this point, the alpha value is still 1, and the beta value is -1. However, because the alpha value is greater than or equal to the beta value, the algorithm can prune the branches corresponding to O's moves, as they cannot possibly result in a score better than -1 for O. This saves significant time and resources, as the algorithm does not have to search through these branches.

In conclusion, alpha beta pruning is a powerful technique for improving the efficiency of game tree search algorithms. By pruning branches that cannot possibly affect the final outcome of the search, the algorithm can save significant time and resources, making it more practical to use in complex games such as chess or Go.