Standard machine in measure theory
You're getting confused because you are getting the "standard machine" mixed up with the monotone class theorem. The standard machine is used when you want to show some property holds for all $L^1$ functions by proving them for indicators and invoking the monotone convergence theorem. The thing is that (at least on the surface), you have to prove the property in question for indicators of general measurable sets.
On the other hand, the monotone class theorem is used to prove properties for general sets of functions (not necessarily the set of all $L^1$ functions) by first showing them for indicators of sets in a generating $\pi$-class. The trade-off here is that the set of functions you are dealing with has to be a monotone class (of functions).
Traditionally, this theorem is stated only for vectors spaces of bounded measurable functions, but as Dembo notes in your link, it's not hard to extend it to general measurable functions.