The current assessment is that using large language model AI chatbots such as ChatGPT to generate indexes does not produce results that come anywhere near meeting standards for excellence in indexing.

These AI chatbots fail at the indexer’s primary task: to ensure readers find needed information. Tests show that AI chatbots typically provide inadequate and inappropriate structure and cross-references, both under- and over-index material, and produce inaccurate indexes that require significant human remediation. Each of these failings is problematic:

• Inadequate or inappropriate index structure, especially with regards to cross-references, prevents readers from effectively navigating to subtopics and related topics while misrepresenting the focus of the book
• Under- and over-indexing produces bloated indexes that omit significant content. Over-indexing increases printing costs and requires readers to wade through irrelevant material to reach desired content; under-indexing prevents readers from locating information and misdirects readers into thinking information isn’t included in the book
• Accuracy issues—including failure to pass a standard test for page reference accuracy and an extremely high rate of issues that require manual remediation—waste readers’ time and breaks the trust between readers and the book

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.*

*This statement was provided by the American Society for Indexing’s Special Interest Group for Digital Publications Indexing on April 7, 2026. It is based on research by the American Society for Indexing’s AI Committee.