Chunking
Breaking content into discrete, self-contained sections that AI retrieval systems can extract and process independently. Well-chunked content is more likely to be surfaced by RAG systems.
Related Terms
Google's AI-generated summary displayed above traditional search results. Appearing in AI Overviews requires content that AI models can easily extract and cite.
Search experiences powered by large language models (ChatGPT, Perplexity, Gemini) that synthesize answers from multiple sources instead of returning a list of links.
Structuring content so AI models are more likely to cite your site as a source when generating answers. Clear definitions, data, and attributable statements improve citation rates.
Robots.txt directives and meta tags that control whether AI training crawlers (GPTBot, ClaudeBot, etc.) can access your content. A new dimension of crawl management.
The process by which AI models verify their generated output against authoritative sources. Well-structured, factual content is more likely to be used for grounding.
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