Answer Engine Optimization (AEO)
A subset of GEO focused on structuring content to directly answer specific questions, making it extractable by AI answer engines.
This term is part of the 3LA EntityMap → /entitymap.html
Related terms
Generative Engine Optimization
coreThe practice of optimizing content to be retrieved and cited by AI-powered answer engines. 3LA's Layer 3 score measures GEO performance as citation rate and sentiment across ChatGPT, Claude, Gemini, Perplexity, and Mistral. Distinct from classical SEO which optimizes only for search engine crawlers.
Query Fan-out
coreA technique used by AI search systems to generate multiple concurrent, related sub-queries from a single user query in order to retrieve more comprehensive results. Example: 'how to fix a weedy lawn' fans out to 'best herbicides', 'remove weeds without chemicals', 'prevent weeds'. Content organised as Atomic Content Units with clear topic coverage is more likely to be retrieved across fan-out queries.
Schema Markup
L2Machine-readable code (JSON-LD using Schema.org vocabulary) added to web pages to help search engines and AI models understand content meaning and context.
L3 AI Layer
coreThe third layer in the Three-Layer Approach framework. Measures 12 metrics including Structural Proof Gap, Citation-Worthiness, Citation Readiness, Answer-First Format, LLMs.txt, and Chunk Optimization. Citation rate measured across ChatGPT (GPT-5.2), Claude Sonnet 4.6, Gemini 3.1 Pro in every audit. Weight: 30% of LayerScore. Platform average L3: 68/100. Typical lift after optimisation: +28 points.
