mlabonne/llm-course — LLM Engineer Track

Type: article / GitHub curriculum (educational) Tier: 4 (Educational) Author(s): Maxime Labonne Date: Accessed 2026-06-01 URL: https://github.com/mlabonne/llm-course Accessed: 2026-06-01

Why This Source Matters

A highly-starred, frequently updated open curriculum that organizes the practitioner's path into three tracks. Its "LLM Engineer" track maps almost one-to-one onto this lab's project sequence, which validates the curriculum ordering and provides a cross-reference for "running LLMs" as the foundational first step (Project 1).

Key Claims

• The course is organized into three parts: LLM Fundamentals (math, Python, neural nets), The LLM Scientist (building optimal LLMs), and The LLM Engineer (creating and deploying applications).
• The LLM Engineer track's first topic is "Running LLMs": accessing models via APIs (OpenAI, Anthropic, Hugging Face) or locally (LM Studio, Ollama); prompt engineering (zero-shot, few-shot, chain-of-thought); structured output.
• Subsequent topics: vector storage & RAG → retrieval-augmented generation → advanced RAG & agents → inference optimization (Flash Attention, KV caching, speculative decoding) → deployment → security (prompt injection, data poisoning).
• The track emphasizes application development over model training.

Relevant To

• concepts: [running-llms, api-access, prompt-engineering, curriculum-sequencing]
• projects: [01-ai-chatbot, 03-semantic-search, 04-pdf-research-assistant, 08-ai-agent]

Notes

• Use as a curriculum cross-reference and for breadth, not for authoritative API details. "Running LLMs" being step 1 of the Engineer track corroborates Project 1's position as the foundation.
• Related curated lists gathered the same session: github.com/HandsOnLLM/Hands-On-Large-Language-Models, github.com/dair-ai/Prompt-Engineering-Guide, github.com/rohitg00/ai-engineering-from-scratch (Phase 12 = LLM Engineering maps to Projects 1–4).