# What Context Engineering Means — Prompt & Context Engineering

Source: https://www.skillbyai.com/en/prompt-context-engineering/f-what

> Prompt wording is one part; deciding what goes in the window is the bigger job.

## Wording versus contents

**Prompt engineering** usually means writing clear instructions: role, task, constraints, format, examples. **Context engineering** is the wider job of deciding **everything the model receives on a call**: which instructions, which tool definitions, which examples, how much conversation history, which retrieved documents, which tool results, in what **order** and **structure**, and within what **token budget**. In real applications most of the window is filled by your code, not by a human typing, so the quality of answers depends heavily on that assembly step. A perfect instruction with the wrong documents still gives a wrong answer.

## Everything the model sees is the prompt

Context engineering designs the whole input: instructions, tools, examples, history, documents and the question.

![Three ideas: scope, tokens, anatomy.](assets/figures/prompt-context-engineering/section-1-map.svg) — Figure 1.1 — Scope, tokens and anatomy.

## Briefing a new colleague

A good brief is not only polite wording: it is choosing which files to hand over, in what order, and what to leave out so they are not buried.

## Log the final assembled prompt

Always be able to see the exact text sent to the model; most bugs are visible there and invisible in your template.

**Quiz:** What does context engineering add beyond prompt wording?

- [ ] Training the model weights
- [ ] Choosing a nicer font
- [x] Deciding which content enters the window, in what order, structure and budget
- [ ] Raising the temperature

*Answer:* Deciding which content enters the window, in what order, structure and budget. The assembled input decides what the model can know on that call.