AI Engineer Roadmap

Overview

Building with LLMs blends classical backend skills with retrieval, evaluation, and safety considerations. This path moves from model basics to production RAG and serving patterns.

Why This Exists

The field changes quickly, but durable concepts—tokenization, embeddings, vector search, prompt contracts, and observability—stay relevant across model generations.

How It Works

1. Model literacy — What LLMs optimize and where they fail.
2. Representation — Embeddings and vector stores as searchable memory.
3. RAG & prompts — Grounded answers and reliable interfaces.
4. Production — Latency, cost, guardrails, and operations.

Suggested sequence

Phase	Focus	Start here
1	Foundations	Introduction, LLM basics
2	Retrieval	Embeddings, Vector databases
3	Applications	RAG architecture, Prompt engineering
4	Shipping	Production LLM systems
Bridge	Platform skills	Backend caching, API scalability

Reality check Prototype with the simplest stack that proves value; add vector databases and complex RAG only when baseline prompts and evaluation justify them.

Resources

• Courses — ML and LLM application courses
• Blogs — labs and product engineering writeups