Now, all of the examples that we looked at for Gen AI uses so far are on top of the utilities
that reside on my website, which I must admit are all kind of small repositories.
This is very different from the products that I work on for my clients, which are kind of,
you know, massive repositories like 1000 plus files or actually products that are spread
across multiple repositories.
And I've observed my own JNAI usage to be very different in such situations.
Unfortunately, I've got nothing to show because these are all client repositories and I am
not authorized to share them in public, but it's worth noting down very important distinctions
in how I've seen myself using JNAI for those situations for repositories that are kind
of very large.
And when I'm unfamiliar with them, I use JNAI a lot to understand that code.
But even for me to ask the right questions to JNAI, I found myself first having to traverse
a lot of those repositories and understand so that I can ask like for it to look at specific
functions or files or, you know, API roots and that sort of thing.
For repositories that are massive, but I'm like well-versed with them because I've been
working on them for long, I find using JNAI very limited.
I actually find coding or at least starting to code the overall framework myself a lot
faster because I understand the context of the design choices we made over a period of
time, the legacy issues or why something is so and so forth.
So I find myself writing the overall framework first and I then use JNAI just to do the basic
filling of gaps of writing a few lines where I'm feeling lazy.
The code is kind of similar to rest of the stuff or fixing smaller exceptions.
So what I understand from these experiences is that as the repositories are larger, the
context that we need to provide to JNAI is a lot larger and that kind of reduces the
effectiveness of JNAI.
So in situations where the context is larger, we cannot have I've not had JNAI move up the
value chain beyond just like the basic error fixing or writing rudimentary autocomplete
or repetitive kind of code.