Context Engineering: The Skill After Prompt Engineering
Context engineering is the practice of deciding what information, instructions and examples an AI system actually needs to do a task well, instead of relying on a clever one-line prompt. As prompting has become table stakes, this is the skill that decides whether AI output is usable on the first try or needs three rounds of fixing.
Why this comes after prompt engineering, not instead of it.
A good prompt asks the right question. Good context makes sure the AI has what it needs to actually answer it.
Prompt engineering optimises the instruction. Context engineering optimises everything the model sees around that instruction: the background information, the examples of what good looks like, the constraints that rule out the wrong kind of answer, and the format the output needs to arrive in to be useful without editing.
Most disappointing AI output is not a prompting failure. It is a context failure. The model was asked a reasonable question with none of the information a competent person would have needed to answer it well.
What good context actually includes.
A short mental checklist, before treating any AI output as a first draft worth trusting.
The real constraints of the task, not just the goal: what has already been tried, what must not change, what the audience already knows. The shape of a good answer, ideally a real example, since a model will match a pattern far more reliably than it will infer one from a description. And the boundary of what the model does not know: internal context, recent decisions, unwritten preferences, all need to be supplied, not assumed.
Building this into a reusable context pack for a recurring task takes real work once, and pays that work back every time the task repeats.
Questions people ask.
- 01
What is context engineering?
Context engineering is the practice of deciding what information, examples and constraints an AI system needs to complete a task well, treating the surrounding context as seriously as the instruction itself. - 02
Is context engineering different from prompt engineering?
Yes. Prompt engineering focuses on how a question or instruction is phrased. Context engineering focuses on what the model has available to answer it well: background, examples, constraints and format. Both matter, but context is usually the bigger lever on output quality. - 03
Who needs to learn context engineering, not just engineers?
Anyone who relies on AI output for real decisions. A leader reviewing an AI-assisted brief, an HR team generating training content, and a developer prompting a coding assistant are all solving the same underlying problem: what does the model need to know to get this right.
Working out how your team should actually use AI?
Workshops and advisory on the practical skills, prompt and context, that decide whether AI output is usable.