Extractability

Extractability is how cleanly a chunk of content can be lifted and reused by an AI system without needing surrounding context to remain accurate or coherent, functioning as a practical measure of whether a passage would still make sense if it were the only thing quoted.

Why it matters

A useful test for extractability is reading any single paragraph in isolation and asking whether it still makes sense without the paragraph before or after it, since that's functionally the same test an AI system's passage-retrieval step applies to every chunk it evaluates.

Related terms

• Answer Block / Atomic Answer

  An answer block, or atomic answer, is a self-contained passage, typically 40 to 100 words, that fully answers a specific question on its own, without requiring a reader or an AI system to reference surrounding paragraphs to understand it.

• Citability

  Citability is how likely an AI engine is to actually quote or attribute a specific passage when constructing its answer, distinct from retrievability, since a passage can be retrieved into an engine's candidate set without ever being judged specific or self-contained enough to quote.

• Chunking

  Chunking is how AI systems split content into smaller passages, typically 200 to 500 tokens, before indexing and retrieving it, meaning a page is rarely evaluated as one continuous whole but as a set of independent segments competing separately for retrieval.

• Retrievability

  Retrievability is the likelihood that a given piece of content gets surfaced at all during an AI system's initial document search, the step before any citation or answer-inclusion decision is made, meaning content can be well-written and still have low retrievability.