What Is AI Engineering?

The short answer

AI engineering is building reliable software features on top of large language models (LLMs) and other foundation models, usually through an API. You don't train the model; you design what goes into it (prompts, context, tools), what comes out (validated, structured output) and everything around it (retrieval, evaluation, guardrails, cost and monitoring).

What you build

• Assistants that answer questions from your company's documents (retrieval-augmented generation).
• Workflow steps: classify tickets, extract fields from invoices, draft replies for review.
• Agents that call tools (search, databases, APIs) in a loop to complete tasks.

feature = ["user request", "prompt + context", "model", "validate output", "tools / retrieval", "evaluate & monitor"]
for i, step in enumerate(feature, 1):
    print(f"{i}. {step}")
print("Only one of these steps is the model; AI engineering is the rest.")

1. user request
2. prompt + context
3. model
4. validate output
5. tools / retrieval
6. evaluate & monitor
Only one of these steps is the model; AI engineering is the rest.

How this tutorial works

Every example is Python you can run in the browser. Where a real model would be called, a small stub model returns fixed replies, so the examples run instantly, for free, and always give the same output. Real API code is shown alongside where it helps.