# What LLM Engineering Is — LLM Engineering Foundations

Source: https://www.geekswithgeeks.com/en/llm-engineering/i-what

> See what changes when a model becomes a component in a product.

## A component that does not behave like a function

A demo needs a good prompt and a few lucky examples. A product needs the model to behave acceptably across **thousands of different inputs**, week after week, within a budget, under attack, and while everything around it changes. **LLM engineering** is the discipline of making that true. It starts from what is different about the component: its output is **non-deterministic** (the same input can give different answers), **hard to specify** (there is no single correct answer to "summarise this"), **costly** (every call has a price and a latency), **opaque** (you cannot step through its reasoning) and **changing** (providers update or retire models). The response is familiar engineering applied with care: clear requirements and metrics, **automated evaluation**, version control for prompts and configuration, tests, **observability**, cost and latency budgets, staged releases, and safety controls. This course covers those foundations. Earlier courses cover the pieces (prompting, RAG, APIs, embeddings, security); here we connect them into a working practice.

## Works on my prompt is not shipped

LLM engineering applies ordinary engineering discipline to a component that is non-deterministic, probabilistic and costly.

![Four steps: define, build, measure, operate.](assets/figures/llm-engineering/section-1-map.svg) — Figure 1.1 — Define, build, measure and operate.

## Demo versus product

What changes when you ship.

```text
Demo                              Product
5 hand-picked examples            thousands of real, messy inputs, including hostile ones
"looks good"                      measured pass rate with a confidence interval
prompt lives in a notebook        prompt + model + settings versioned and reviewed
one happy path                    retries, fallbacks, timeouts, "I don't know" paths
cost: ignored                     cost per resolved task tracked against a budget
no monitoring                     traces, dashboards, alerts, feedback loop
safety: hope                      least privilege, validation, approvals, red-team tests
```

## Start with the failure you fear most

Ask what a wrong answer would cost, and let that set how much testing and control the feature needs.

**Quiz:** Which property of LLM components most changes how we test them?

- [ ] They always run on laptops
- [ ] They are written in Python
- [x] Outputs are non-deterministic and have no single correct answer
- [ ] They never change

*Answer:* Outputs are non-deterministic and have no single correct answer. We therefore measure behaviour statistically over many cases.