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Track 4 — AI Engineering

AI Engineering

Twenty-three chapters across seven parts — foundations, LLM engineering, RAG, agents, production (serving, observability, safety, cost), 5 worked examples, and a senior-level interview chapter. Built for engineers switching into AI roles.

Foundations · Python for AI engineering

2 min read · 6 sections

Foundations · The math + ML survival kit

2 min read · 5 sections

Foundations · Transformers from first principles

2 min read · 5 sections

LLM Engineering · Provider landscape and tradeoffs

2 min read · 6 sections

LLM Engineering · Prompt engineering that works

2 min read · 6 sections

LLM Engineering · Structured output and tool use

2 min read · 5 sections

LLM Engineering · Cost, latency, and reliability

2 min read · 5 sections

RAG Systems · Embeddings and semantic search

2 min read · 6 sections

RAG Systems · Vector databases — choosing and operating

2 min read · 5 sections

RAG Systems · Chunking and retrieval strategies

2 min read · 8 sections

RAG Systems · Evaluating RAG and LLM systems

2 min read · 6 sections

AI Agents · ReAct loop and core patterns

3 min read · 8 sections

AI Agents · Multi-agent orchestration

3 min read · 6 sections

AI Agents · Memory architectures

3 min read · 7 sections

AI Agents · Reliability — failure modes and guardrails

2 min read · 5 sections

Production · Serving LLM applications

2 min read · 6 sections

Production · Observability and tracing

2 min read · 6 sections

Production · Safety — prompt injection, jailbreaks, PII

2 min read · 5 sections

Production · Fine-tuning when prompting hits a wall

2 min read · 6 sections

Production · Cost engineering at scale

2 min read · 4 sections

AI engineering — 5 worked examples

7 min read · 5 sections

Career · The AI engineering interview

3 min read · 7 sections

Master reading list

2 min read · 5 sections