AI glossary for businesses
Prompt engineering
In short
Prompt engineering is the practice of designing, testing and improving the instructions you give an AI model so that it produces reliable, useful results. It combines clear wording with techniques such as giving context, showing examples, asking for a fixed format and breaking a task into steps. For most businesses it is a working skill, not a technical job.
How prompt engineering works
A single prompt is one request. Prompt engineering is the process around it: you write a prompt, run it on real cases, look at what went wrong and adjust. Think of a recipe card in a restaurant kitchen. The goal is that any cook on any shift produces the same dish. A well-engineered prompt does the same for AI output across different people and days.
Common techniques that work across ChatGPT, Claude and Gemini:
Give a role and context: "You are writing for a Greek family-run hotel. Guests are mostly couples from Germany."
Show examples: two or three samples of the output you want. This is often called few-shot prompting.
Fix the format: headings, bullet points, word limits or a table.
Split the task: first an outline, then the draft, then a check against a list of rules.
Set boundaries: "If information is missing, say so. Do not invent prices."
In practice for a small business
A five-person marketing team sends a monthly newsletter in Greek and English. Instead of each person writing prompts from scratch, they build a small prompt library in Notion: one prompt for the newsletter intro, one for product highlights, one for subject lines. Each prompt includes the brand tone rules and two approved examples. They test every prompt on three past newsletters before using it. When the AI tool updates its model, they rerun the tests.
A library like this often makes results more consistent and cuts editing rounds, though how much depends on the team. Measure it during a pilot.
Why it matters and what to watch out for
It is a team skill: the EU AI Act requires companies to take steps for sufficient AI literacy among staff, a duty in force since 2 February 2025. Basic prompt training is a practical part of that. See how to prepare your team for AI adoption.
Models change: a prompt that works today may behave differently after an update. Keep test cases.
It has limits: no prompt fixes missing knowledge. If the AI needs your price list or policies, you must provide them.
Data: do not put personal data into shared prompt templates.