The discipline of structuring language to maximize machine legibility and retrieval probability. Unlike traditional communications (an intuitive craft), Linguistic Engineering treats language as infrastructure that must be engineered to survive the semantic supply chain of AI intermediaries while maintaining fidelity to original intent.
The likelihood that an AI-generated answer accurately reflects the source material. High probabilistic fidelity means content is structured to be cited, not remixed. Low fidelity indicates vulnerability to misattribution, context collapse, and semantic drift.
The mutation of a brand narrative as it passes through generative AI models. Semantic drift occurs when AI systems compress, transform, or redistribute content, causing qualifiers to be lost, technical terms to be oversimplified, or source attributions to disappear. The Drift Score metric quantifies the semantic distance between original intent and AI-mediated output.
An economic model where value is created by synthesizing answers (AI) rather than distributing links (Search). In the Inference Economy, AI models do not 'find' content—they manufacture answers from it. Organizations must engineer language for retrievability, citability, and resilience through the semantic supply chain.