In the context of search algorithms, a heuristic functions as a guiding principle that simplifies decision-making by estimating the least cost to reach a goal from a particular state. This strategic filtering is crucial for optimizing search procedures by prioritizing more promising paths over less hopeful ones. Two significant types of heuristics are admissible heuristics, which guarantee that the actual cost to a goal will never exceed the estimated cost, and consistent heuristics, which ensure that the estimated cost from a node to a goal is always less than or equal to the cost to a successor node plus the estimated cost from that successor to the goal. For example, in route-finding problems, the straight-line distance between two points serves as an admissible heuristic, as it never exceeds the actual distance required to travel. Overall, heuristics are fundamental to enhancing the efficiency of informed search strategies in AI.