The best way to achieve a good abstraction is to recall what the word meant before computer science: namely, something closer to generalization.
In computing, we emphasize the communicational (i.e. interface) aspects of our code, and, in this respect, tend to focus on an "abstraction"'s role in hiding information. But a good abstraction does more than simply hide detail, it generalizes particulars into a new kind of "object" that is easier to reason about.
If you keep this in mind, you'll realize that having a lot of particulars to identify shared properties that you can abstract away is a prerequisite. The best abstractions I've seen have always come into being only after a significant amount of particularized code had already been written. It is only then that you can identify the actual common properties and patterns of use. Contrarily, abstractions that are built upfront to try and do little more than hide details or to account for potential similarities or complexity, instead of actual already existent complexity are typically far more confusing and poorly designed.
In computing, we emphasize the communicational (i.e. interface) aspects of our code, and, in this respect, tend to focus on an "abstraction"'s role in hiding information. But a good abstraction does more than simply hide detail, it generalizes particulars into a new kind of "object" that is easier to reason about.
If you keep this in mind, you'll realize that having a lot of particulars to identify shared properties that you can abstract away is a prerequisite. The best abstractions I've seen have always come into being only after a significant amount of particularized code had already been written. It is only then that you can identify the actual common properties and patterns of use. Contrarily, abstractions that are built upfront to try and do little more than hide details or to account for potential similarities or complexity, instead of actual already existent complexity are typically far more confusing and poorly designed.