The current assessment is that using large language model AIs (LLMs) such as ChatGPT to generate indexes does not produce results that come anywhere near meeting standards for excellence in Indexing.

 

These LLMs fail at the indexer’s primary task: to ensure readers find needed information. Tests have shown that LLMs typically underindex book-length works, do not provide adequate structure or cross-references, and can insert false information (hallucinations) into the index.** Each of these is problematic:

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• Severe under-indexing, with as few as 20-40% of the access points of human-generated indexes, can prevent readers from locating desired information and mislead them into thinking that omitted information isn’t in the book at all.

• Absent/near-absent index structure, especially cross-references, prevents the reader from effectively navigating to subtopics and related topics while misrepresenting the focus of the book.

• Hallucinations (including invented page references and even wholly nonexistent topics) waste the reader’s time and break the trust between the reader and the book.

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Future developments in AI may bring improvements, but at present the human brain of a professional indexer is still the best tool for analyzing, writing, and editing an index in full awareness of context as per quality standards.

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Unless otherwise requested by a client, I do not use AI for any aspect of my indexing and editing work.

 

*Statement adapted from DPI-SIG Statement on the Role of AI in Indexing (as of July 1, 2025).

**Bartmess, Elizabeth, “AI: Where You Can Use It and Where You Shouldn’t,” May 31, 2025, ISC/SCI Conference 2025 “Location! Location! Location.”