Prompt Engineering
The craft of phrasing LLM prompts to get relevant and accurate responses.
Full definition
Prompt engineering is the craft and methodology of formulating queries (prompts) for large language models to get maximally relevant, accurate, and useful answers.
Why it matters
LLM response quality directly depends on prompt quality. The same question phrased differently can produce vastly different results — from superficial to expert-level.
Core techniques
- •Role prompting — assign the model a role: "You are a senior SEO analyst with 10 years of experience." This narrows context and raises expertise.
- •Few-shot — provide 2-3 examples of the desired output format. The model replicates the pattern.
- •Chain-of-thought — ask the model to "think step by step" before answering. Quality jumps 30-50% on analytical tasks.
- •Structured output — ask for JSON, tables, or markdown. The model organizes its thinking.
- •Constraints — specify limits: "no more than 200 words", "only verified sources", "no fluff".
Connection to GEO optimization
Understanding prompt engineering helps you optimize content for AI search. If you know how users phrase questions to ChatGPT — you write pages that answer exactly those phrasings.