Answer Engine Optimization (AEO)

AEO is the successor discipline to SEO. Where traditional SEO optimizes for ten blue links on a Google results page, AEO optimizes for being the cited source inside an AI-generated answer. The economic stakes are higher because answer engines collapse the funnel: a single citation can replace a hundred clicks.

Effective AEO combines four ingredients: structured data (FAQ, Article, Organization, HowTo schema), machine-readable brand context (the llms.txt file), entity authority (consistent NAP, Wikipedia/Wikidata presence, author E-E-A-T), and question-intent content written in the natural-language style that LLMs were trained on. Tools like VibecodeAEO measure how often each AI engine cites your brand for prompts that matter and surface the specific content gaps to close.

Related terms

• llms.txt — A plain-text file at the root of a domain (like /robots.txt) that gives AI systems a curated, machine-readable summary of your brand, products, and key documentation links.
• AI Visibility Score — A 0-100 metric measuring how often and how favorably your brand is mentioned across major AI answer engines for a tracked set of buyer-intent prompts.
• Answer Engine — An AI system that responds to natural-language queries with a synthesized answer rather than a list of links — examples include ChatGPT, Perplexity, Google Gemini, Microsoft Copilot, and Anthropic Claude.
• Schema Markup — Standardized JSON-LD or microdata embedded in HTML that tells search engines and AI systems the meaning of page content (e.g., this is a product, this is a FAQ, this is a person).
• E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) — Google's framework for evaluating content quality. AI engines now use similar signals to decide which sources to cite.