Model Context Protocol (MCP)

Agentic search is the next stage of AI search: instead of returning a single synthesized answer, an AI agent autonomously plans and executes a multi-step research task — fetching pages, comparing sources, running calculations, and producing a structured deliverable. ChatGPT Agent, Perplexity's Deep Research, and Google's Project Mariner are all early agentic-search products. Why it matters: Agentic search radically expands the volume of pages an AI engine consults for a single user question — from a handful of sources for a chat answer to dozens or hundreds for an agent task. That means more total citation opportunities for well-structured, citation-worthy content, and a sharper penalty for sites that bots can't easily fetch.

Multimodal citations are AI-engine answers that incorporate not just text but also images, charts, video clips, and audio passages — each with its own source attribution. Google AI Mode and Gemini already surface inline images and video thumbnails alongside text answers, and ChatGPT Search regularly cites image sources. Why it matters: A brand whose visual assets (charts, infographics, product photos, founder portraits) carry proper alt text, structured-data attribution, and clean, fast-loading hosting becomes citable across more answer surfaces — capturing visibility that text-only optimization misses entirely. Multimodal-citation readiness is fast becoming a core AEO and GEO requirement.

ChatGPT is the conversational AI assistant developed by OpenAI, launched in November 2022, that interprets natural-language questions and generates synthesized written answers using large language models (currently the GPT-4 and GPT-5 family). With the addition of ChatGPT Search, it now actively browses the live web and cites external sources directly inside its responses, making it one of the most influential answer engines alongside Google AI Overviews and Perplexity. Why it matters: For brands, ChatGPT is no longer just a chatbot — it is an active referral source and reputation surface. When prospects ask ChatGPT about a service, an industry, or a specific company by name, the brands that get cited inside the answer win the trust transfer and the click-through. Earning ChatGPT citations requires the same foundations as Answer Engine Optimization: third-party validation from authoritative outlets, complete schema markup, comprehensive FAQ content, and a public llms.txt file that tells AI crawlers what your site is authoritative on. Brands invisible to ChatGPT in 2026 are increasingly invisible to their own prospects.

DeFi (Decentralized Finance) is the category of blockchain-based financial applications — lending, borrowing, trading, derivatives, stablecoins, yield products — that operate via smart contracts on public blockchains rather than through traditional banks, brokers, or custodians. Major DeFi protocols include Uniswap, Aave, Compound, MakerDAO, and Curve. Why it matters for PR and reputation: DeFi is the most heavily scrutinized vertical in crypto, sitting at the intersection of securities law (the SEC has signaled multiple DeFi products meet the Howey Test), consumer protection (FTC and state regulators), and on-chain risk (every exploit is publicly traceable within minutes). DeFi PR and reputation management therefore operates inside a tighter compliance perimeter than general crypto: every claim about yield, risk, decentralization, or token utility is a potential enforcement input. The protocols that survive long-term build PR programs around verifiable on-chain data, audited smart contracts, and transparent governance — not marketing claims about returns.

Gemini is Google's family of multimodal large language models that powers Google AI Mode, AI Overviews, the Gemini consumer app, and Google Workspace AI features. Gemini models can reason over text, images, audio, video, and code simultaneously, and they integrate tightly with Google Search, Google Knowledge Graph, and YouTube. Why it matters: Because Gemini is the engine behind every Google AI surface, optimizing for Gemini citation is effectively optimizing for the majority of branded AI search traffic in the United States. Brands earn Gemini visibility through Knowledge Graph entity strength, schema markup, high-authority backlinks, and content that answers questions in structured, citation-ready prose.

Semantic chunking is the process AI engines use to split a web page into meaningful, self-contained units (chunks) before embedding them for retrieval. Unlike naive fixed-length chunking, semantic chunking respects paragraph, section, and topic boundaries — so each chunk represents a coherent idea rather than an arbitrary character window. Why it matters: When an LLM retrieves your content to answer a query, it pulls chunks, not pages. Pages written as a sequence of well-bounded, single-topic passages with clear headings produce cleaner chunks, which produce higher retrieval scores, which produce more citations. Structure beats length.