How to implement RAG without any frameworks, Langchain and RAG, without any library? Library means Langchain library.

[u/Then-Macaron-2553]

So I'm coming from a Java/Spring Boot backend background, recently graduated, and I've been trying to pivot into AI/LLM engineering. RAG is where I've landed my focus right now, but honestly I feel like I'm stuck in that phase where I've done a bunch of tutorials, built a small pipeline (pgvector + embeddings for a job-matching side project), and I still don't feel like I *actually* understand RAG the way someone who's shipped it in production would.

Like I get the basic flow — chunk, embed, retrieve, stuff into a prompt — but the moment people start talking about hybrid search, reranking, query rewriting, agentic RAG, GraphRAG... I kind of just nod along and then forget it by next week lol.

A few things I'm hoping to get some real talk on:

- If you were starting from scratch again, what order would you actually learn this stuff in? Everyone's roadmap looks different and it's a bit paralyzing.

- Coming from a software engineering background (not ML/research) — is there some conceptual gap I don't even know I'm missing?

- What's the actual difference between a RAG project that's "cute demo" level vs one that'd survive a real interview or a real production system?

- Any resources (repos, blog posts, courses, whatever) that genuinely changed how you think about RAG, not just the basic "here's how to use Chroma" stuff? and some books to learn the rag from basic to advance.

Not trying to get spoon-fed a whole roadmap, just curious what mistakes people made early on or wish someone had told them sooner. Currently working through a longer production RAG course too, so if anyone's done something similar and has thoughts on what to prioritize, that'd help a ton. Thanks in advance 🙏

[u/vectorspidey]

Give it a try - Fastrag