I've noticed something in my various endeavors to research topics: people tend think in examples rather than categories.
If I am attempting to investigate categories of operations, I will find thousands of specific examples.
Finally, I realized that to discover categories, start from the objective and work through means to achieve it in categories. Working both up and down the conceptual pyramid is a beneficial skill.
A workable, real life example would help me understand in concrete terms what you mean in the abstract.
@UncleIroh
Example:
-> Searching for methods of processing fruit to ensure it is shelf-stable.
-> Thousands of recipes for jams and jellies
-> Objective: cultivate a situation in which microbes cannot affect the foodstuffs, often by removing sufficient water or by making the vessel sterile or inhospitable.
I will mention that it's one thing to ask for an example to ground an abstract as you did, but it's another to never consider the abstracts, which seems to be the default for most.
The reason why I'd do it this way is that I'd personally find it more productive to speak with a domain expert from the start.
That' would be my number 1 preference since it means you get to ask questions at a different level.
So domain expert first, if I have access to one, and AI second since it's an almost perfect use-case for up-to-date AI
Finding the 1000's of examples and doing the abstraction yourself is the least efficient, but still useful.
@DoubleD
I think I understand.
In your example, I can think of 2 main ways of arriving at categorical knowledge:
1. find the 1000's of specific examples & do the categorization yourself. Do this if you have no prior knowledge and you're good at abstraction.
2. frame your initial queries in categorical terms. Do this if you already know there are categorical levels to fruit processing.
Personally I'd use AI (https://pi.ai) as an entry-point into (2) & then drill down to specifics.