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AI Glossary

Prompt Engineering

June 22, 2026

Prompt Engineering is the practice of designing and refining prompts to get more accurate and useful results from AI models.

Prompt Engineering

Prompt Engineering is the practice of crafting inputs (prompts) to get the best possible output from an AI model. It combines clear communication with an understanding of how models behave.

What it means in plain English

Prompt engineering is the skill of asking AI well. It isn’t about secret magic words; it’s about giving the model a clear brief: who it should act as, exactly what you want done, the context it needs, and the format you expect. Good prompt engineering also includes techniques like providing examples of the output you want, asking the model to reason step by step for complex tasks, and refining across a conversation rather than expecting a perfect first answer.

As models improve, they need less hand-holding — but the core principle holds: specificity and context in, quality out. It remains one of the most practical skills for anyone using AI tools.

A simple example

Instead of “summarise this,” a prompt engineer writes: “You are an editor for busy executives. Summarise the text below into three bullet points, each under 15 words, focusing on decisions and next steps.” The added role, format, and focus produce a far more useful summary.

Why it matters

Prompt engineering is the difference between people who find AI “hit or miss” and those who get consistently excellent results. Because the same model can produce mediocre or outstanding work depending purely on how it’s asked, this skill is the highest-leverage thing most people can learn about using AI.

Frequently asked questions

What is prompt engineering?

It is the practice of crafting and refining prompts to get better, more reliable results from AI models, using techniques like clear instructions, context, examples, and step-by-step reasoning.

Is prompt engineering still important?

Yes — even as models improve, how you frame a request strongly affects the output, so effective prompting remains a valuable, practical skill.

Frequently Asked Questions

It is the practice of crafting and refining prompts to get better, more reliable results from AI models, using techniques like clear instructions, context, examples, and step-by-step reasoning.

Yes — even as models improve, how you frame a request strongly affects the output, so effective prompting remains a valuable, practical skill.

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