u/No-Butterscotch9679

Okay so I've been doing backend stuff for a while now. Python, FastAPI, PostgreSQL, Docker, currently building a Dropbox clone while learning AWS on the side. That part I'm pretty comfortable with.

Also been trying to learn ML properly on weekends, not just copy pasting sklearn. Did linear regression and gradient descent from scratch, working through logistic regression right now.

But here's the thing, I actually want to get into GenAI. Not the research side, just building real stuff with LLMs. RAG pipelines, agents, that kind of thing.

So I started looking at LangChain and honestly it feels pretty natural coming from a backend background. It's basically just abstractions over API calls which makes sense to me. But I don't know if I'm jumping ahead too fast or if this is actually fine.

Like do I need to grind through all of classical ML first before any of the GenAI stuff clicks? Or can I just run both in parallel and it'll be okay?

Also is LangChain even a good starting point or does it hide too much and I'll regret not learning the raw API stuff first?

Just want honest takes from people who've actually done this, not a roadmap.

I’m currently learning FastAPI for backend since I come from a Python background, and I’m starting to understand how things work. Alongside that, I’m also learning ML because I genuinely enjoy it, especially the math side.

So far in backend, I’ve covered things up to authentication and authorization. I also know Docker for deployment. I understand databases at a basic level (queries, joins, etc.), but I wouldn’t say I’m advanced yet.

At this point, I feel like I’m somewhere between upper beginner and early intermediate. I’m also fairly confident that I could switch to another framework and still build APIs if needed.

My question is — am I going in the right direction, or should I consider changing my stack? And what should I focus on next to improve?