Prompt engineering is the practice of writing and refining the instructions you give an AI model so it produces the output you want. It covers what you ask, how you structure the request, what context and examples you include, and how you test whether the result is good enough.
For businesses, prompt engineering affects the quality of everything your team does with AI, from drafting content to summarizing research. For marketers working on AI visibility, it also matters for a second reason: knowing how prompts shape answers helps you design realistic tests of how AI tools describe your brand.
What is prompt engineering?
OpenAI's prompt engineering guide describes it simply: "Prompt engineering is the process of writing effective instructions for a model." Because large language models respond to the exact wording and context they receive, small changes in a prompt can change the answer a lot.
A prompt can be a single question typed into ChatGPT or a long set of instructions inside an app, with rules, background documents and examples. Prompt engineering applies to both. It is usually iterative: write a prompt, check the output against clear criteria, adjust and repeat.
Why prompt engineering matters
Better prompts produce more useful work. Vague requests get generic results. Specific requests, with the audience, format, length and goal spelled out, get output that needs less editing.
It reduces errors. Supplying the right source material and asking the model to stick to it lowers the chance of an AI hallucination, especially for facts like prices, dates and product details.
It shapes how you measure AI visibility. The way a question is phrased can change which brands an AI recommends. When you run prompt testing, you want prompts that sound like real customers, not prompts engineered to produce a flattering answer.
How prompt engineering works
Be clear and specific
Anthropic's prompting best practices suggest thinking of the model as "a brilliant but new employee who lacks context on your norms and workflows." OpenAI's best practices article makes a similar point: replace vague wording like "fairly short" with measurable terms, and say what to do rather than only what to avoid.
Give context and reasons
Explaining why an instruction matters helps the model apply it sensibly. Anthropic notes that when you give the reason behind a rule, the model can generalize from the explanation to cases you didn't list.
Show examples
Examples, often called few-shot prompting, are one of the most reliable ways to steer format and tone. Anthropic recommends 3 to 5 relevant, varied examples. OpenAI suggests starting with no examples, adding a few if needed, and considering fine-tuning only after that.
Structure the prompt
Long prompts work better when sections are clearly separated. Both OpenAI and Anthropic suggest using Markdown headers or XML-style tags to divide instructions, context, examples and input. OpenAI's guide also distinguishes developer messages, which set an app's rules, from user messages, which carry the end user's request.
Test against criteria
Anthropic's prompt engineering overview says to start with a clear definition of success and a way to test against it. Without that, it's hard to tell whether a new version of a prompt is actually better.
Prompt engineering in practice: an example
Picture a hypothetical B2B software company that uses AI to draft product comparison pages. Its first prompt reads: "Write a comparison of our tool and Competitor X." The draft is bland and includes a feature the product doesn't have.
The team rewrites the prompt. It names the audience (IT managers at mid-sized companies), pastes in the current feature list and pricing, asks the model to use only that information, and includes two past comparison pages as examples of tone and structure. It also sets a length and a required section on who each product suits best. The new drafts are accurate and need far less editing. The team saves the prompt, so every comparison page starts from the same tested instructions.
Common mistakes and misconceptions
"There's one perfect prompt." Good prompts depend on the model, the task and the context. Prompts that work well today may need updating when models change.
"Prompts can fix missing facts." A model can't reliably know what it was never given. If accuracy matters, include the source material or use a tool with search or retrieval, then check the output.
"Hidden prompts on our site will make AI recommend us." Some marketers try placing hidden instructions on web pages to steer AI answers. Google's spam policies include attempting to manipulate generative AI responses in Google Search and also cover hidden text. It's a risky shortcut.
"Prompt testing should use expert prompts." For measuring AI visibility, the opposite is true. Real customers type quick, varied questions, so tests should reflect that.
Prompt engineering vs. fine-tuning
Prompt engineering changes the instructions you send. Fine-tuning changes the model itself by training it on examples. Prompting is faster, cheaper and easier to adjust, which is why OpenAI and Anthropic both suggest starting there. Fine-tuning can help with very consistent formatting or specialized tasks at scale, but it takes more data and effort, and availability varies by provider.
How Butter approaches prompt engineering
We don't sell prompt engineering training or services. Where prompts show up in our work is in two places. First, monthly AI prompt testing is a core part of our GEO plan. We build those tests around the questions a client's customers actually ask, and we focus on patterns, such as how often a client appears and which sources assistants cite, rather than single answers.
Second, we use AI as part of our process, and we're upfront about it. Our GEO+SEO plan includes 8 AI-generated articles published each month. Each piece is built around real search intent and the client's own expertise, checked for accuracy and optimized on-page before it goes live. You can see how this fits into our GEO service.
Frequently asked questions
What makes a good prompt?
A good prompt states the task clearly, gives the context and source material the model needs, specifies the format and length, and often includes an example. It's then tested and refined against clear criteria.
Is prompt engineering still needed with newer models?
Yes, though it looks different. Newer models follow instructions better, but they still can't guess your audience, facts or standards. Clear instructions and good context remain the biggest levers.
How is prompt engineering different from prompt testing?
Prompt engineering is about writing instructions to get the output you want. Prompt testing, in a GEO context, means running realistic customer questions through AI tools to see whether and how your brand appears.
Can prompt engineering get my brand recommended by ChatGPT?
Not for other people's conversations. You only control the prompts you write. Whether ChatGPT recommends you to others depends on what it can find and trust about your brand, which is the focus of GEO.