AI search & GEO

prompt engineering

Also called: prompt, prompting, prompt design

Prompt engineering is the practice of writing and refining the instructions you give a large language model so it consistently produces the output you want. It covers how you phrase a request, the context and examples you include, and the role or system instructions that steer the model's response.

Most prompts start bare (Summarize this article in three bullets) and get better through iteration. OpenAI and Anthropic both describe prompting as test-and-refine work: write a prompt, check the output against real examples, adjust, repeat. A few levers do most of the work.

Core techniques

Two things people miss. First, more words is not better. Precise instructions that spell out the exact logic and data a task needs beat long, vague ones. Second, not every problem is a prompt problem. Anthropic points out that goals like lower latency or cost are often solved by switching models, not by rewording the input.

For businesses, why this matters has shifted. Prompting is no longer only a developer skill. The prompts that now decide your visibility are the ones your customers type into ChatGPT, Gemini, and Perplexity when they look for what you sell.

How it affects your traffic

Prompt engineering shapes traffic in a way most SEO guides skip. Your buyers now type prompts into ChatGPT, Gemini, and Perplexity instead of running a Google search, and you cannot prompt your own site into those answers. What you can do is structure content the way these models read a request, so your pages become the source an assistant retrieves and cites. That is the work of AI SEO (also called GEO). If you rank on Google but never surface in AI answers, the gap is usually structural and fixable.

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