LLM Engineering Course

Learn how language models work by building the important pieces yourself, then use those pieces to develop a model for a realistic company use case.

Start with setup and the learning guide. The guide names prerequisites and checkpoints; the glossary explains unfamiliar terms. No prior machine learning is required.

Two entry paths

Standalone path: includes Python, mathematics, neural-network, and PyTorch foundations.
Godot RL bridge: uses a diagnostic to skip concepts already demonstrated in the Godot RL course.

Both paths start with Unit 0, continue through the diagnostic and learning-path check, and converge before text tokenization. Every skipped foundation keeps a re-entry link.

The standalone foundations track F01–F06 covers Python/NumPy, tensor shapes, probability and cross-entropy, a trainable neuron, MLP/backpropagation, a tiny autograd engine and a PyTorch checkpoint. Godot-RL graduates take the B01 bridge and prove only the missing LLM-specific concepts. Both paths continue through the core: the T01 bigram baseline, T02 byte-level BPE, the T03 data pipeline and embeddings, T04 self-attention, the T05 Mini-GPT decoder and its T06 core gate, I01 decoding, sampling and the KV cache, then E01 reproducible training, E02 data quality, E03 evaluation and E04 profiling. The company track C01–C04 turns a requirement into a model decision, auditable data, protected evaluation and continued pretraining; the adaptation track A01–A04 covers SFT/LoRA, the adaptation comparison, DPO and the RLHF bridge; the capstone ties them into a reversible company-model decision. The Applied: RAG track (R01–R02) is a standalone entry that needs only Python and one LLM call.

Every required outcome has a CPU route. Run the hardware and cost preflight before choosing a larger profile.

What you will build

a bigram language-model baseline;
a BPE tokenizer;
self-attention and a decoder-only transformer;
a small GPT-style model with training and evaluation;
instruction and preference fine-tuning;
a synthetic-data pipeline with quality gates;
an optimized local inference service;
a reproducible company-model capstone.