Growth Marshal Will Make You AI’s Go-to Answer

AI Search Glossary

Key terms and concepts used throughout this page [https://www.growthmarshal.io]

AI Search Optimization is the practice of engineering digital content for retrieval, validation, and citation within AI systems. It combines entity signals, structured data, and answer-first content to increase citation probability across large language models. Also referred to as Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), and Artificial Intelligence Optimization (AIO).

Answer-First Content is a content architecture pattern that leads with direct, quotable answers before supporting context. Answer-first content places the extractable claim in the opening sentence, making it easier for LLMs to retrieve and cite without parsing surrounding material.

Authority Graph™ is Growth Marshal's verification-layer framework that establishes a business's presence in the knowledge graphs and structured databases AI systems query to confirm existence. Authority Graph™ turns external registries into independent proof of legitimacy and expertise.

Citation is a direct reference to a source within an AI-generated response. In AI Search Optimization, citation rate measures how often a business is named or linked when LLMs answer relevant queries.

Content Arc™ is Growth Marshal's content-architecture framework that structures pages for how LLMs retrieve information. Content Arc™ engineers answer-first headers, modular sections, and citation-ready copy that models can parse, quote, and attribute. Learn more →

Entity is a distinct, unambiguous concept that AI systems recognize as a discrete unit of knowledge—such as a person, organization, product, or place. Entities have defined relationships to other entities and carry attributes that models use to determine relevance.

Entity API™ is Growth Marshal's identity-layer framework that makes a business machine-readable to large language models. Entity API™ combines structured data, direct LLM instructions, and canonical fact sources into a unified layer AI systems query when retrieving information. Learn more →

JSON-LD (JavaScript Object Notation for Linked Data) is a structured data format that encodes entity information in a way machines can parse. JSON-LD markup enables AI systems to extract verified facts about a business without relying on natural language interpretation.

Knowledge Graph is a structured database that represents entities and their relationships as interconnected nodes. AI systems query knowledge graphs—including Google's Knowledge Graph, Wikidata, and industry-specific registries—to verify facts and establish entity identity.

Knowledge Graph Anchors are verified connections between a business entity and authoritative knowledge graph entries. These anchors signal to AI systems that an entity exists, is legitimate, and has defined relationships within a broader information network.

Large Language Model (LLM) is an AI system trained on large text datasets to generate human-like responses. LLMs—including ChatGPT, Claude, Gemini, and Perplexity—retrieve and synthesize information to answer user queries, making them a new surface for business discovery.

llms.txt is a standardized file that provides large language models with structured information about a website and its entities. Similar to robots.txt for search crawlers, llms.txt gives AI systems a canonical source for retrieving accurate facts. View Growth Marshal's llms.txt →

Machine-Readability is the degree to which content can be parsed and interpreted by automated systems without human intervention. Machine-readable content uses structured formats, consistent patterns, and explicit relationships that AI systems can process programmatically.

Retrieval is the process by which AI systems identify and extract relevant information to include in generated responses. Unlike traditional search ranking, retrieval determines whether a business appears in an AI answer at all, not its position in a list.

Semantic Wiring is the network of topical associations that connect an entity to relevant concepts in an AI system's understanding. Strong semantic wiring increases the probability that a business will be retrieved when users ask questions within its domain.