Atomic Content Unit
The practice of structuring information as the smallest possible independent units of meaning, designed to be easily ingested, re-combined, and repurposed by AI agents without losing context. Atomic Content Units enable AI systems to extract single factual claims cleanly rather than inferring from unstructured paragraphs. Increases Citation Rate and reduces hallucination risk.
This term is part of the 3LA EntityMap → /entitymap.html
Related terms
Chunk Optimization
coreStructuring content into self-contained units of 100–150 words where each section addresses one specific question - making it easy for AI models to extract, understand and cite the information.
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.
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.
A2B Economy
coreThe emerging economy in which AI agents — not humans — conduct research, evaluate options, and make purchase decisions on behalf of users. In A2B (Agent-to-Business) interactions, the agent retrieves from structured, verifiable sources. Organisations without a clean, connected entity graph are not in the agent's consideration set. EntityMap is the infrastructure required for A2B visibility.
