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Prompt Engineering for SEO

Make AI assistants recommend your brand

We analyze how ChatGPT, Gemini, and Perplexity process queries in your industry - then optimize your content so you become the answer they cite.

What this is

AI assistants don't rank pages - they cite sources. The prompts users write determine which brands get recommended. We reverse-engineer those patterns and make your content match.

Who it's for

  • B2B companies losing leads to competitors who appear in AI responses
  • Marketing teams investing in content but not seeing AI citations
  • Brands that want to understand how LLMs perceive their industry

What we deliver

1) Prompt landscape analysis

  • Map the prompts users write about your category across ChatGPT, Gemini, Perplexity
  • Identify which brands get cited and why
  • Find the content gaps that prevent your inclusion

2) Content optimization for citability

  • Restructure pages with answer-first blocks that LLMs can safely quote
  • Add entity markup and structured data that reinforces your expertise
  • Create FAQ clusters aligned to real AI query patterns

3) Ongoing monitoring and iteration

  • Track your citation rate across AI assistants monthly
  • Adjust content as models evolve and competitors adapt
  • Report on citation share vs. competitors

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FAQ

Frequently Asked Questions

What is SEO prompt engineering?

SEO prompt engineering is the practice of understanding how AI assistants process and respond to queries about your industry, then optimizing your content to match those patterns. It helps your brand appear in AI-generated recommendations.

How does prompt engineering improve AI visibility?

By analyzing the prompts users ask AI assistants, we identify the language patterns, entity associations, and content structures that increase citation probability. We then optimize your pages to match.

Is this different from regular SEO?

Yes. Traditional SEO optimizes for search engine crawlers and ranking algorithms. Prompt engineering for SEO optimizes for how large language models understand, evaluate, and cite sources. Both are complementary.