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Capability

As explained in How Large Language Models Work

A capability is an ability or functional competence that a system can demonstrate.

A capability is an ability or functional competence that a system can demonstrate. For LLMs, capability describes what the model can produce or accomplish, while reliability asks how consistently and accurately it does so. section 1.9 section 7.0

LLM capabilities span several kinds of work:

  • Translate languages, transcribe speech, read text in images or printed documents, and support search or information retrieval. section 9.1
  • Generate new language and, in broader generative AI applications, images, music, or videos. section 9.1 section 1.9
  • Handle many tasks through one model, although broad capability does not guarantee dependable results or human-like understanding. section 9.1 section 7.0

Capability versus certainty

An LLM may translate text, summarize information, or generate an answer, yet fluent output is not by itself evidence of intelligence, factual accuracy, or reliable reasoning. section 7.0 section 1.9

How How Large Language Models Work covers it

How deep it goes
Defines capability and surveys applications, while stopping short of detailed mechanisms or formal measurement.
Examples
Uses translation, transcription, document reading, search, and media generation as concrete capability examples.
What you can do after
Evaluate/understand LLM applications by separating what a model can produce from how reliably it produces it.
How it connects
Connects capability with reliability, LLMs, generative AI, machine translation, speech-to-text, optical character recognition, and information retrieval.

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This explainer was generated by Manning's knowledge graph from the text of How Large Language Models Work. The section links point to the parts of the book it draws on.