Reference · Learning Paths

find your route

30+ sections, one you. Pick the path that matches your goal — AI Engineer, Backend, Interview prep, or just the LLM track — and follow it in order. The rest is optional depth.

🧭 Navigation guide186 lessons mapped🎯 All levels

How to use this pageThe course has 30+ sections — you do NOT take them all in order. Pick the path that matches your goal; each lists the sections in order, with the rest as optional depth. Every section climbs essential → tech-lead, so you can stop at the rung you need.

Absolute beginner? Start here

If you're new to programming, do these first regardless of your eventual goal — they're the foundation everything else assumes:

Foundations (everyone)

Python Fundamentals (P1–P6) — the language.
Developer Foundations (DF1–DF5) — terminal, Git, the web, packaging.
Software Testing (TQ1–TQ5) — write code you can trust & change.

Path 1 · AI / LLM Engineer

The flagship path — build production LLM applications and agents. ~Core: Foundations → Prompting → RAG → Agents → Evaluation/Production → the Capstone, then the Claude/Anthropic, LangChain, and Anthropic-Skills tracks. Depth: Inference, Fine-tuning, Safety, and a cloud track (AWS or Local Models).

Order	Section	Why
1	Foundations (above)	you must be able to build + test
2	Prompting, RAG, Agents (ch1–ch4)	the core LLM skills
3	Evaluation & Production (ch5–ch6), FDE capstone	make it real
4	Claude & Anthropic, Anthropic Skills	the model + agent tooling
5	LangChain/LangGraph, Multi-Agent	frameworks & orchestration
6	Inference, Fine-tuning, Safety, AWS/Local	expert depth

Path 2 · Backend / Platform Engineer

For general software engineering strength (with AI as a specialty later). Emphasizes CS foundations and delivery:

Backend route

Foundations (Python, DF, TQ).
Data Structures & Algorithms (D1–D8).
System Design (SD1–SD7) — SQL, LLD, HLD.
Containers & Deployment (CD1–CD5).
MLOps/LLMOps + Data & App Building for breadth.

Path 3 · Interview prep (job hunt now)

Targeting interviews in the next few weeks:

Interview sprint

DSA (D1–D8) — the coding problems.
System Design (SD1–SD7) — the design problems.
Career & Interview Prep (CR1–CR5) — resume, behavioral, and the interview process.
Review the projects you'll talk about (see the portfolio guide).

Path 4 · Just the LLM track (already an engineer)

Experienced engineers who only want the AI parts can skip the foundations and go straight to Prompting → RAG → Agents → Evaluation, then Claude/Anthropic, LangChain, and whichever expert track (Inference / Fine-tuning / Local / AWS) fits your stack.

Rough time estimatesFoundations ≈ 3–5 weeks part-time. Each expert track ≈ 1–2 weeks. The full AI-Engineer path is a ~4–6 month part-time journey to production-ready; the interview sprint is ~3–4 focused weeks on top of skills you already have.

Keep open while you work

Cheat sheet, Glossary, Roadmap, and the portfolio guide.

Educational content, provided as-is and without warranty. Code samples are examples — review, test, and adapt them before using in production. See the Terms of Use & Disclaimer. Use at your own risk.