AI engineering, from models to systems
Understand the systems behind the API.
Original lessons on model internals, production infrastructure, and engineering judgment for senior and staff engineers building with AI.
8 learning tracks · 60 lessons live · self-paced, no account
Start here
Available lesson drafts, beginning with the transformer trace.
Inference primers
Two short pieces that set up the vocabulary before the numbered course.
Learning tracks
3 tracks have lesson drafts available; the other tracks remain planned.
- Track 01In progress
LLM Internals & Architecture
Implement and inspect a decoder-only transformer, explain how it learns and generates, and evaluate the design choices behind modern and reasoning models.Transformer architecture · Tokenisation and embeddings · Attention and masking · Positional encoding · Pretraining and generation · KV cache and context · MQA, GQA, and MLA · Mixture of Experts · Multi-token prediction · Architecture comparison · Reasoning evaluation · Reasoning distillation - Track 02In progress
Inference Optimisation & AI Infrastructure
Make models fast and affordable to serve: memory layout, batching, quantisation, and the GPU behaviour behind every latency number.FlashAttention · KV cache · Quantisation · Batching · GPU performance · Local and distributed serving - Track 03In progress
Fine-Tuning & Domain-Specific Models
Adapt a base model to a domain without losing the judgement you started with: datasets, adapters, distillation, and small models.Dataset engineering · Supervised fine-tuning · LoRA and QLoRA · Distillation · Small language models - Track 04Planned
RAG & Agent Systems
Build systems that retrieve, decide, and act, with retrieval quality, orchestration, and failure handling treated as first-class engineering problems.Retrieval · Reranking · Orchestration · Tool execution · Memory · Multi-agent coordination - Track 05Planned
AI Evaluation, Reliability & MLOps
Turn “it seems better” into evidence: evaluation sets, observability, versioning, and release gates that survive real traffic.Evaluation frameworks · Observability · Versioning · Deployment · Regression gates · Governance - Track 06Planned
Reinforcement Learning & Self-Improving AI
Understand how models learn from feedback: reward design, preference optimisation, and the loops that make agents improve without drifting.Reward design · Preference optimisation · RLHF · Feedback loops · Agent improvement - Track 07Planned
System Design & Distributed Systems
The durable systems skills behind every AI product: storage, queues, consistency, concurrency, and capacity under failure.Databases · Storage · Caching · Messaging · Consistency · Concurrency · Capacity planning · Reliability · Migrations - Track 08Planned
Staff+ Engineering: Judgment, Strategy & Leadership
The work that compounds beyond code: framing decisions, writing clearly, aligning teams, and connecting engineering to the business.Architectural decisions · Technical writing · Cross-team execution · Technical strategy · Influence · Mentoring · Business alignment