You read a post about LoRA fine-tuning. Then you see one about RAG that mentions embeddings you don't understand. So you search for embeddings, find a vector database guide that assumes you know about quantization, dive into a quantization post that references RLHF concepts you've never seen. Three hours later, you're reading about diffusion models with no clear path back to your original goal.

This is the classic LLM learning trap. The field moves at breakneck speed, posts proliferate across dozens of blogs, and everything connects to everything else. Without a structured path, you accumulate fragments instead of building a coherent mental model. You know isolated techniques but struggle to see how they fit together into production systems that actually work.

TLDR: This roadmap organizes 37 LLM Engineering posts into decision-tree learning paths based on your goal: ship an app fast (App Developer), customize models (ML Engineer), build autonomous agents (Agent Builder), or understand the theory (Research Track). Start with fundamentals, then choose your path.