Introduction
What is AI engineering?
AI engineering is building reliable products on top of frontier models you did not train - grounding them in your data, giving them tools, measuring whether they work, and shipping something that holds up when real users touch it. Coding agents write most of the code; your job is systems thinking and judgment.
Read the full introduction in pre-courseStart with a step-by-step guide to what AI engineering is, then install your coding agent and get ready for Session 1.
What you keep
A clear mental model of AI engineering - reliability around models you did not train - plus a working coding-agent workflow.
You ship
Cursor or Claude Code installed, GitHub + OpenAI key ready, and a capstone one-liner before the first live session.
What is AI engineering? (start here)
A step-by-step guide: what AI engineering is, what we assume, what we teach, and what to do next before Session 1.
Install Cursor or Claude Code (do this next)
Pick one AI coding agent, install it, open a project folder, and confirm you can chat with it. This is the highest-leverage pre-course step.
Python, Git, and API keys
Let your coding agent check Python, create a venv, and scaffold FastAPI. Create a GitHub account, add your OpenAI API key, and keep secrets out of chat.
Seed your capstone
Pick the one project you will build across five live sessions - your Session 1 `/ask` endpoint is its first capability.
Building in public
Why we share builds openly, where to post (LinkedIn, Substack, share it), and how to talk about the skills you learn.
Pre-session check-in
Share your setup status and capstone one-liner before Session 1.
Session 1 checklist
What you must have ready before the first live session - and what is fine to finish during or after it.
What you keep
How to turn a model call into a dependable, observable piece of software - plus context engineering, token optimisation, and a free-resources bridge to mastery.
You ship
A working `/ask` endpoint that answers reliably - the reasoning core of your capstone.
Watch through (async)
Think Like an AI Engineer (free 10-part series)
- 1How LLM Actually Works (in Plain English)Watch
- 2How to Prompt AI Like a Pro: The 5-Part FormulaWatch
- 3How to Pick the Right AI Model for the JobWatch
- 4Using AI With Your Files, Images and DataWatch
- 5Context Is Everything: The #1 Skill for Using AIWatch
- 6Vibe Coding for Non-Engineers (and Engineers)Watch
- 7What AI Agents Actually Are (Hype vs Reality)Watch
- 8Build AI Workflows With No Code (n8n, Zapier, Make)Watch
- 9AI Evals: How to Know If Your AI Actually WorksWatch
- 10The Fastest Way to Stay Current in AIWatch
Live session resources
Week 1 live session slides
Foundations deck: model call → reliable component, Playground, FastAPI /ask, structured outputs, guardrails, token economics, model selection.
Open resourceWeek 1 `/ask` demo code (5 stages)
FastAPI starter repo: bare /ask through structured output, guardrails, model routing, and cost readout. Includes Streamlit demo UI and stage smoke tests.
Open resourceFrom model call to reliable component
Production means the right shape, at a known cost, with a plan for when it fails - every time.
The OpenAI API and the Playground
Experiment in the Playground first: system vs user messages, temperature, max tokens, and the token counter.
Module 1.1 - Build your first LLM service
Stand up a real POST endpoint with typed models before any theory - the living thing everything else improves.
Module 1.2 - Prompting that ships
Structured outputs, few-shot, and decomposition - three techniques to make output deterministic enough to build on.
Module 1.3 - Output guardrails and validation
Schema, semantic, and safety validation - plus retry patterns - between the model and everything downstream.
Module 1.4 - Token economics and context budgets
Tokens are money and latency. Treat the context window as a budget, not free space.
Module 1.5 - Choosing the right model
Match capability, cost, and latency - and route different steps to different models in one system.
Module 1.6 - Context engineering: the throughline
Prompting, cost, and model choice are all facets of one skill: engineering what the model sees.
Module 1.D1 - Advanced structured outputs and function-calling schemas
Nested schemas, enums, and function calling - when the shape must be airtight and feed code directly.
Module 1.D2 - The provider landscape beyond OpenAI
Anthropic, Gemini, open-weight models - per-task fit and portability across providers.
Module T - Token Optimisation
Nine concrete techniques to cut cost and latency without trading away quality you need.
Module B - Mastering AI Engineering: the free-resources bridge
A curated map from bootcamp graduate to self-directed mastery - almost everything is free.
Week 1 assignment support: Ship Your First AI Endpoint
Copy-paste prompts to deploy your Week 1 POST `/ask` endpoint on Render and prove it with curl + guardrails.
Ship your reliable reasoning endpoint
Deploy a live POST /ask, prove it with curl, post on LinkedIn about how you learned and what you shipped.
Make your /ask endpoint answer from your documents, not training data. Running example: Northwind Robotics policy corpus throughout.
What you keep
How to make a model answer from your data instead of its training, and why naive RAG fails — plus Claude Code skills, MCP, and eval habits that make the build repeatable.
You ship
Week 1 /ask extended with a vector store, POST /ingest for new docs, RAG query with citations + refusal, and a golden-set eval on your live Render URL.
Recommended reading (async)
RAG foundations — watch & read (async)
Live session resources
Week 2 live session slides — RAG + Vector Databases
Session 2 deck: why RAG exists, embeddings, chunking, vector DBs, live LangChain build, failure modes, evals, and homework.
Open resourceWeek 2 live session notebook (GitHub)
Hands-on RAG notebook: embeddings, chunking, vector DBs, LangChain Document Q&A build, and evaluation. Clone and run with your OpenAI key.
Open resourceNorthwind Robotics demo corpus (sample_docs)
Four policy documents for live demos and homework: handbook, expenses, security, product spec.
Open resourceEmbeddings intuition
Similar meanings sit close together in vector space — retrieval quality depends on this mental picture.
Build naive RAG, and watch it fail
Embed, store, retrieve, stuff into prompt — then break it instructively on the Northwind corpus.
Chunking
How you split documents decides what retrieval can find — most bad RAG is bad chunking.
Reranking and hybrid search
Reranking reorders candidates by relevance; hybrid search catches exact terms embeddings miss.
RAG evals
Measure retrieval and generation separately — wrong chunks vs right chunks ignored.
Graph RAG
When knowledge has structure and relationships, plain vector RAG leaves value on the table.
Multimodal RAG
Retrieve over images, tables, and diagrams — not just prose.
Claude Code layers — skills, MCP, subagents, and plugins
One reference for which layer does which job: CLAUDE.md, skills, MCP, subagents, hooks, plugins.
Week 2 assignment support: RAG on your Week 1 endpoint
Copy-paste prompts to add POST /ingest, vector store, retrieval-first testing, and RAG on POST /ask.
Week 2 homework: RAG on your Week 1 endpoint
Extend POST /ask with a vector store, POST /ingest for new docs, retrieval-first testing, and a golden-set eval.
What you keep
What an agent really is, how to build the loop by hand, and how to orchestrate one reliably.
You ship
Your assistant turned into an agent that plans and uses tools.
The agent loop, by hand with the raw SDK
Think, pick a tool, call it, observe, decide again - no framework mystification.
Orchestration with LangGraph
Nodes, edges, shared state - durable execution and human-in-the-loop checkpoints.
Tools and MCP
MCP is how modern agents get tools - connect to servers instead of hand-wiring every integration.
Multi-agent and A2A
Several specialists when roles are genuinely separable - most problems do not need multiple agents.
Prompt injection, where guardrails get dangerous
Malicious instructions in retrieved content can hijack tool-using agents.
Turn your capstone into an agent
LangGraph agent with at least one real tool - trace each step of the decision loop.
What you keep
The full TRACE loop as a system you build - Trace, Read, Analyze, Codify, Enforce.
You ship
An eval suite your capstone runs against.
The vibe-check trap
"It works on the examples I tried" is not evaluation - and for non-deterministic systems it is dangerous.
Trace and Read: error analysis
Capture full records, then read traces by hand - qualitative research on real failures.
Analyze: cluster, count, prioritize
Binary pass/fail judgments, ranked by frequency and impact - the spec for your eval tooling.
Codify: build the eval suite
Code-based assertions plus validated LLM-as-judge - an unvalidated judge is just another vibe check.
Enforce: evals in the loop
Wire evals into the development loop so every change gets measured automatically.
Build and run TRACE on your capstone
Trace, read, analyze, codify, and wire evals to run on demand - show a metric moving after a fix.
What you keep
How to give an agent memory that lasts and improves, and how to run it persistently.
You ship
Your agent remembering across sessions and deployed to run for real.
The memory spectrum
In-context, retrieval-based, and structured memory - pick the right mechanism for what you need.
GBrain: graph plus vector hybrid memory
Vector recalls by similarity; graph recalls by relationship - the hybrid earns its complexity when both matter.
Hermes Skills and compounding
Packaged abilities the agent accumulates - each capability makes the next task easier.
Persistent runtime deployment
From "I built an agent" to "I deployed an agent" - runs continuously, holds memory, acts on triggers.
Give your capstone memory and deploy it
Persistent memory across sessions and a real URL - not your laptop.
No new teaching - finish so your system ships instead of stalling.
Build-support office hours
Bring a concrete blocker - a failing eval, a flaky tool, a deployment error.
Final system brief and production-readiness checklist
From working to demo-ready: system brief, walkthrough plan, and ship checklist.
Demo Day
Submit live system + URL (mandatory). Present the grounded, agentic, evaluated, memory-holding stack.
Course resources
Videos and articles — also embedded on the Week 2 module page.
- Full series: Think Like an AI Engineer (10 parts)
- Part 1: How LLM Actually Works (in Plain English)
- Part 2: How to Prompt AI Like a Pro: The 5-Part Formula
- Part 3: How to Pick the Right AI Model for the Job
- Part 4: Using AI With Your Files, Images and Data
- Part 5: Context Is Everything: The #1 Skill for Using AI
- Part 6: Vibe Coding for Non-Engineers (and Engineers)
- Part 7: What AI Agents Actually Are (Hype vs Reality)
- Part 8: Build AI Workflows With No Code (n8n, Zapier, Make)
- Part 9: AI Evals: How to Know If Your AI Actually Works
- Part 10: The Fastest Way to Stay Current in AI
AI Engineering learning resources
A curated reference for students. Every link is a primary source or a practitioner-written guide, favouring free material where possible.
- LLM Engineer roadmap (GitHub)
Best curated roadmap. Follow the LLM Engineer path (not LLM Scientist) plus the LLM Engineer's Handbook.
- The AI Engineering Curriculum: 5 Tracks, Ranked and Sequenced
Production-focused, updated in the last 12 months. Blunt take: RAG is how most companies ship AI in 2026.
- Neural Networks: Zero to Hero (Andrej Karpathy, YouTube)
Build LLMs and transformers from scratch - understand what breaks and why.
- LLMs from scratch (rasbt, GitHub)
Pairs with Karpathy for hands-on code alongside the videos.
- Generative AI for Beginners (Microsoft, GitHub)
21 lessons on prompt engineering, responsible AI, and LLMOps with code samples.
- Generative AI Engineering with LLMs (IBM / Coursera)
Free to enroll. Hugging Face, PyTorch, RAG, LoRA and QLoRA. Needs working Python and ML basics.
- LLM Engineering (Ed Donner, Udemy)
Most popular paid hands-on option - 8 weeks, 20+ models, RAG, QLoRA, agents.
- LLM evaluations course roundup (Evidently AI)
Free ~1 hour track: evaluation methods, LLM judges, guardrails, building eval datasets.