Glossary
Retrieval Behavior
How AI systems retrieve, rank, and select content passages for responses
Context Window Fit
The optimization constraint requiring content to be efficiently structured so key information fits within AI context windows while maintaining completeness, maximizing the likelihood of being fully processed and accurately represented in AI outputs.
Embedding Relevance
The practical scoring mechanism by which AI systems measure semantic similarity between content passages and user queries through vector proximity in high-dimensional embedding space.
Freshness Signals (AI)
The temporal relevance indicators that influence how AI systems weigh, prioritize, and select content based on recency, including publication dates, update frequencies, and time-sensitive markers that affect retrieval ranking and citation likelihood.
Passage-Level Ranking
The retrieval paradigm where AI systems evaluate, score, and select individual content sections or passages rather than entire pages, fundamentally changing how content must be structured for AI visibility.
Query Intent Matching (LLM-Level)
The semantic alignment between user queries and content as interpreted by large language models, going beyond keyword matching to understand the underlying purpose, context, and expected outcome of a query.