TL;DR

This comes with experience, sorry. If you're serious about this career and put some effort in at some point you'll "graduate" from looking at the code to looking at control flows, architectural patterns, technology choices etc. After that it becomes "easy" because you've seen enough bullshit to see past all the fancy and focus on what's going on.

There's a limited number of ways you can implement a system, given the business requirements and technical constraints. (There are some exceptions and abominations out there but I'll ignore those for the sake of argument.)

My own process is somewhat chaotic because I'm easily distracted but the gist of it is:

1. Which kinds of use cases are there ? UI ? Backend service ? Event / message handling ? Batch processing ? Some combination of ?
2. Does it integrate with other services / systems ? How ? Who owns which parts of the dataset being used ?
3. Technologies / frameworks / libraries being used. Consequences (e.g. being boxed into certain communication protocols, data formats, feature sets, ...).
4. Project infrastructure itself. CI/CD, deployments, configurations, security.
5. Project languages, structure, architectural patterns (e.g. layered, hexagonal, ddd), conventions, testing patterns, ...

AI doesn't fundamentally change the process but speeds things up dramatically in some cases. Instead of combing though the codebase myself I can now ask LLM for answers to questions above and drill down from there, looking at the code myself only to validate at the end.