What you'll build
Seven projects and a language model of your own
Each phase ends in something you build and run yourself, in Python, in your browser. Alongside the course, Baby AI grows from an alphabet into a small transformer.
Phase 0 · Tools
Ad-copy reviewer
Combine files, functions, JSON and an LLM call into one small tool.
- Break a real task into load, check, report and generate steps
- Write rule checks as small functions that return a list of problems
- Save results as a JSON report other tools can read
See the lesson Free
Phase 1 · Building with LLMs
Research agent
A marketing research agent with retrieval, tools and evals.
- Combine retrieval, tools and an agent loop into one assistant
- Make every claim traceable to a source the agent actually looked at
- Grade the agent automatically with code checks on a small eval set
Phase 2 · Math, when you need it
Cross-entropy and KL divergence
The loss function language models are trained on.
- Compute cross-entropy between a true distribution and a model's prediction
- Show that with one correct answer, cross-entropy is −log of the probability given to it
- Compute KL divergence and explain it as the extra cost of using the wrong distribution
Phase 3 · Machine learning
Churn prediction
Predict which customers will leave, and measure it properly.
- Run a complete ML project: framing, baselines, model comparison, one honest test
- Translate model scores into a campaign: lift, a cost-based contact list and its expected value
- Explain what drives the predictions and check whether the probabilities can be trusted
Phase 4 · Deep learning
Train a classifier
Build, train and debug a network end to end.
- Prepare tabular data for a neural network without leaking test information
- Run the sanity checks before trusting a training run
- Train a regularized network with early stopping on a validation metric
Phase 5 · Transformers
Build a tiny GPT
Write and train a small GPT from scratch.
- Prepare a corpus for a language model: tokenize, split, and batch with shifted targets
- Train a small GPT from scratch and watch its loss fall
- Generate text from it and judge what it has and hasn't learned
Phase 6 · Modern LLM engineering
A tuned, evaluated assistant
Fine-tune, evaluate and deploy a specialist assistant.
- Run a release review: train a candidate, evaluate it on every criterion, and apply release gates
- Diagnose a failed candidate and fix it by changing the training data
- Explain why a single headline metric, or a raw loss, can hide regressions
Baby AI
Grow your own language model
Six stages, each adding one real component of a modern language model. It trains on text you choose, right in your browser.
- Stage 1 · Newborn Your AI has just been born.
- Stage 2 · Infant The simplest way to guess what comes next: look back at the training text and count.
- Stage 3 · Toddler Counting works for pairs of characters, but it can't grow into longer memories.
- Stage 4 · Child One character of memory isn't much.
- Stage 5 · Juvenile A fixed window treats every earlier character the same.
- Stage 6 · Adolescent This is the architecture behind every modern large language model, in miniature: the transformer.