Tutorials · Chapter C (3/4) · ~8 min

Training vs inference

Try it → see it → read → next

Training = studying for the test. Inference = answering a new question.

Try yourself

Playground

Training factory vs inference kitchen

Toggle modes. Training builds the cookbook; inference cooks tonight’s meal.

01Collect datamillions of examples
02Adjust weightsslow · expensive
03Save a modelcookbook ready

Happens in big datacenters — rarely while you chat.

Recap

What you just did

You sorted predict vs generate ideas. Underneath both sits a lifecycle: train a model (expensive, offline-ish), then run inference (cheap-ish, per request).

Teach

How it works

See it

Training time vs chat time

Training

Huge data→Heavy compute→Weights

Inference

Your prompt→Frozen model→Reply

Training = long study · Inference = quick answer from what it already learned

See it splits the two clocks: training (heavy study in a datacenter) vs inference (your prompt → frozen model → reply). Fine-tuning and RAG change what happens at answer time without full retraining — later lessons.

Use it

When you'd use this

Explaining cloud bills (“inference calls”).
Understanding why ChatGPT doesn’t permanently learn your secret password from one message (unless a product stores and trains on it — check the policy).

Watch out

Some products do log chats for improvement. “Inference isn’t training” doesn’t mean “nothing is saved.”

Try next

Try this next

Use the Try yourself playground above first — click around until the win banner appears.

Say aloud: “Training built the cookbook; inference cooks tonight’s meal.”