Prompt engineering
Prompt engineering is the craft of formulating instructions to an AI model so it consistently produces the desired output.
Prompt engineering is the systematic design of instructions (prompts) you give to an LLM so the output is predictable, useful and repeatable. A good prompt usually contains a clear role ('you are an SEO specialist'), a concrete task, relevant context, desired output structure and examples of expected answers. Within marketing and GEO, prompt engineering matters for both productivity (efficient content creation with AI) and measurement (standardised prompts to test how your brand appears in AI engines).
Example
Sloppy prompt: 'Write something about Bitcoin mining.' — generic output. Well-designed prompt: 'You are an SEO specialist for the Bitcoin sector. Write a 120-word introduction for Dutch mining operators about the impact of the 2024 halving on ASIC efficiency. Use the term ‘break-even hashrate’. End with one concrete follow-up question.' — immediately usable copy.
Frequently asked questions
What are the key prompt techniques?
Role assignment, few-shot prompting (2–3 examples of desired output), chain-of-thought ('think step by step'), format specification (JSON with fields X, Y) and output constraints (max words, language, tone). Combining beats any single technique.
Is prompt engineering still relevant as models improve?
Yes. Models get better at interpreting vague prompts, but they can't guess your preferences, brand voice or goals. The more specific context and output specs, the more consistent and reusable the result.
Related terms
Further reading
- → Our service: AI training for marketers