Prompting is a skill, not a trick
The viral 'magic prompt' threads miss the point. Reliable AI output comes from structure, context and evaluation — all of which can be taught.
By Jonathan Lee
Search for “best ChatGPT prompts” and you will find thousands of one-liners promising magic. They mostly disappoint, because they treat prompting as a trick rather than a skill.
The three levers that actually matter
When we teach prompting, we focus on the levers that move output quality predictably:
- Structure — clear instructions, explicit format, and worked examples.
- Context — giving the model the right information, retrieved or supplied.
- Evaluation — knowing how to tell whether the output is actually good.
The third is the one people skip, and it is the one that separates hobbyists from practitioners. If you cannot measure quality, you cannot improve it.
A simple structure that travels
A prompt that works across tasks tends to share a shape:
Role: who the model should act as
Task: the single, specific thing to do
Context: the inputs, constraints and examples
Format: exactly how the answer should be returnedThis is not glamorous, but it is reliable — and reliability is what production work needs.
Why this is teachable
Because prompting is a skill, it responds to deliberate practice. Give a team a rubric, a set of real tasks, and fast feedback, and their output quality climbs week over week. That is exactly how our Prompt Engineering Intensive is built.