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Accuracy

As explained in Introduction to Generative AI, Second Edition

Accuracy is the proportion of predictions or outputs that are correct for a specified task.

A legal brief can sound convincing and still cite cases that never happened. Accuracy means that an output is correct for the task at hand. For a large language model (LLM) drafting a brief, fluent wording does not establish that its legal claims are accurate. section 5.8

In Mata v. Avianca, an attorney used ChatGPT to prepare a brief without checking its claims against reliable sources. The incident illustrates a practical limit: LLMs can produce plausible prose, but they cannot generally ensure factual accuracy. Verification matters especially in work that requires specialized knowledge, including law, medicine, and finance. section 5.8

How Introduction to Generative AI, Second Edition covers it

How deep it goes
Discusses factual correctness and verification, without defining or calculating a classification accuracy metric.
Examples
Uses a legal brief containing invented cases and mentions risks in medicine and finance.
What you can do after
Evaluate/understand it by questioning plausible LLM claims and checking them against reliable sources before professional use.
How it connects
Links accuracy to factuality, large language models, and verification against reliable sources.

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This explainer was generated by Manning's knowledge graph from the text of Introduction to Generative AI, Second Edition. The section links point to the parts of the book it draws on.