What Is AI Engineering?

Question 1

The clearest structural difference between ML engineering and AI engineering is that:

Accepted Answer

ML engineering ends at a trained model; AI engineering begins at one — Classical ML starts from data and produces weights. AI engineering starts from weights someone else produced and builds a product around them — which relocates every hard problem.

Question 2

In AI engineering, the dominant cost moves from training to:

Accepted Answer

Inference, paid per call forever — You did not pay to train the model, but you pay for every token it generates, for every user, for as long as the product runs. That asymmetry drives routing, caching and model-size decisions.

Question 3

Which rung of the adaptation ladder should you try first for a new problem?

Accepted Answer

Prompting, because it is free and instantly reversible — Effort and control rise together up the ladder. Prompting costs nothing to try and nothing to undo, and it resolves far more cases than teams expect before they have measured.

Question 4

A model gives wrong answers about your company's internal refund policy. This is most likely:

Accepted Answer

A knowledge gap fixed by putting the policy in context — Your internal policy was never in the training data, so the model is not failing to reason — it is answering about something it has never seen. Retrieval solves this; a bigger model does not.

Question 5

The three layers of the AI stack, from closest to the user downward, are:

Accepted Answer

Application development, model development, infrastructure — Application development sits closest to the user and is cheapest to change; model development is below it; infrastructure is the layer nearly everyone rents.

Question 6

Why is evaluation harder in AI engineering than in classical supervised ML?

Accepted Answer

Outputs are open-ended, so there is often no single correct answer to compare against — A classifier has a label to check against. An open-ended answer may be correct in many forms, so you have to define what good means and then build something that measures it.

Question 7

A team skips prompting and context work and goes straight to finetuning. The most likely consequence is:

Accepted Answer

A frozen behaviour they do not fully understand, plus a training run to repeat on every base-model upgrade — Finetuning encodes a behaviour before anyone has established what the behaviour should be, and creates a permanent maintenance obligation against future base models.

Question 8

Which of these is NOT typically part of an AI engineer's week?

Accepted Answer

Pretraining a foundation model — Pretraining belongs to the model-development layer and to labs. The defining AI-engineering skill is building a reliable system on a component you did not train.

Question 9

Prompt caching and structured outputs started as application-layer workarounds and are now provider features. The general lesson is:

Accepted Answer

Workarounds for model limitations have a shelf life as layers absorb each other — Each layer absorbs the one above as it matures, so anything you build purely to route around a model limitation should be treated as temporary scaffolding.

Question 10

The practical test for whether a quality complaint is a context problem or a capability problem is:

Accepted Answer

Whether a well-informed human with the same context would have answered correctly — If a person with that context would have got it right, the information was missing and retrieval fixes it. If not, it is a capability ceiling and needs a different model or a decomposition.

	ML engineering	AI engineering
Starting point	···	···
Core artifact	···	···
Hardest problem	···	···
Evaluation	···	···
Iteration speed	···	···
Where cost lives	···	···

AI engineering or ML engineering?

Test your knowledge

The clearest structural difference between ML engineering and AI engineering is that:

The short version

In more depth

What Is AI Engineering?

The reversal

The three layers of the stack

The adaptation ladder

Why the demo is not the product

Interview Questions

References & Further Reading

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