Generators use the `yield` statement to produce a sequence of values lazily, allowing for more memory-efficient iteration. Unlike regular functions, they maintain state between calls.

Python generators are a special type of iterable that allow you to produce a sequence of values lazily using the yield statement. Unlike regular functions that return a single value and exit, generators maintain their state between calls, enabling them to produce a series of values over time. When a generator function is called, it returns a generator object without executing the function's body. Each time the next() function is called on the generator object, execution resumes from the last yield statement, allowing the function to continue running until it hits another yield or raises a StopIteration exception. This behavior makes generators particularly memory-efficient, as they do not store the entire sequence in memory; instead, they generate each value on-the-fly. For example, you can create a generator for Fibonacci numbers:

def fibonacci_gen():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b

Generators are useful for working with large datasets or streams of data, as they allow you to process items one at a time, reducing memory consumption and enhancing performance.