Retrieval-Augmented Generation
A technique used to improve the quality, accuracy, and freshness of AI responses by grounding them in retrieved source documents. RAG systems retrieve relevant content from indexed sources before generating a response. EntityMap improves RAG output quality by providing structured, pre-verified entity relationships and evidence chunks rather than requiring the model to infer relationships from unstructured prose.
This term is part of the 3LA EntityMap → /entitymap.html
Related terms
AI Crawlability
L3The ability of LLM agents and AI crawlers to access, parse, and understand a website's content for use in training or real-time retrieval.
Citation Readiness
L3The degree to which content is structured, authoritative, and clear enough to be cited by an AI model as a source in a generated answer.
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.
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.
