How to Index Your Book with AI

Indexing a book is a difficult and tedious task. One must first read the entire manuscript, note down indexable terms, and then edit the resulting index to ensure that it is accurate, useful, and within budget. This process can take a significant amount of time, especially for first time indexers. Authors indexing their own books may find it particularly challenging, as they may be too close to the material to expect what a reader might look for. Additionally, indexing a book can take anywhere from 10 to 50 hours of manual labor depending on the length and complexity of the manuscript, which can be a significant time investment for authors who are already busy with other aspects of the publishing process or are academics with limited time.

Indexing is still a crucial part of the publishing process, as it allows readers to easily find specific information within a book. A well-crafted index can enhance the usability and accessibility of a book, making it more valuable to readers. However, given the time and effort required to create an index, some authors may choose to hire professional indexers or use indexing software to assist with the process. This can help ensure that the resulting index is accurate, comprehensive, and user-friendly, while also saving time for the author.

At the same time, hiring a professional indexer can be extremely expensive, with costs ranging from $4 to $8 per page or more depending on the length and complexity of the manuscript. This can be a significant financial burden for authors, especially those who are self-publishing or have limited budgets. For a 250 page book, the cost of indexing can range from $1,000 to $2,000 or more.

For too long the indexing process has been a barrier for authors, particularly those who are self-publishing or have limited budgets. The high cost and time investment required to create a book index can discourage authors from including one in their books, which can ultimately limit the accessibility and usability of their work.

An index isn't a list of every term in a book, but rather a carefully curated selection of terms that are relevant and useful to readers. A good index requires a deep understanding of the content of the book, as well as an understanding of what readers might be looking for when they use the index. This is why indexing is such a difficult task for computers - until recently, it has been difficult for AI systems to understand what exactly should go in the index and what shouldn't. Term frequency alone isn't enough to determine what should be included in an index, as many important terms may only appear a few times in a book, while less important terms may appear frequently.

For example, in a book about the history of the Roman Empire, the term "Julius Caesar" may only appear a few times, but it would be an important term to include in the index. On the other hand, a term like "Roman Empire" may appear hundreds of times, but it would not be useful to include in the index, since the entire book is about the Roman Empire and would probably end up as a string of numbers citing every other page in the book.

Furthermore, phrasing subentries such as

is a complex task that requires a level of reading comprehension that basic computer systems have struggled with until recently. Deterministic systems that rely on rules and algorithms have been unable to capture the nuances and complexities of indexing, which has made it difficult for them to produce high-quality indexes. They can identify potential indexable terms based on frequency or other heuristics, but they often struggle to determine which terms are actually relevant and useful for readers, and how to organize those terms in a way that is intuitive and easy to navigate.

However, much of this problem has been more or less entirely solved as of late 2025, with the advent of more advanced AI Large Language Models that are capable of understanding the content of a given text, and determining which terms are relevant and useful for readers. Whatever your stance on AI is, it is undeniable that these modern LLMs have a far superior grasp of the human language than previous Natural Language Processing (NLP) systems, and are capable of understanding the nuances and complexities of language in ways that previous systems could not. This allows them to identify relevant terms and organize them in a way that is more intuitive and useful for readers, which is a crucial aspect of indexing. By leveraging the capabilities of these advanced AI systems, we can create indexes that are not only accurate and comprehensive, but also well-organized and easy to navigate, which can significantly enhance the usability and accessibility of a book for readers.

There is now an opportunity to revolutionize the indexing process and make it more accessible to authors of all backgrounds and budgets. By leveraging AI technology, we can create a more efficient and cost-effective indexing process that allows authors to easily create high-quality indexes for their books without the need for extensive manual labor or expensive professional services. This has the potential to democratize an important part of the publishing process and make it easier for authors to share their work with readers around the world.

But how exactly does one use AI for book indexing?

Uploading a manuscript to an AI chatbot and asking it to generate an index is not as simple as it may seem. For example, ChatGPT and other general purpose LLM chatbots limit the amount of text that can be input at one time, which makes it difficult to use them for indexing an entire book. Additionally, modern AI systems' output tend to degrade in quality as the length of the input increases, which can lead to inaccurate or incomplete indexes. While these problems have improved significantly in recent years, they still present significant challenges for using AI for indexing tasks.

We agree with the American Society for Indexing's conclusion that ChatGPT and similar general purpose chatbots are not currently suitable for indexing tasks. However, their tests have only involved using these systems in their default configurations in a consumer-facing interface, which isn't necessarily the best way to use AI for indexing. Anthropic's, Google's and ChatGPT's consumer web interface heavily limits the capabilities of the underlying AI model, and isn't meant to be used for long, complex tasks like indexing. The underlying AI models that power ChatGPT and similar systems have the potential to be used for indexing tasks, but they would need to be fine-tuned and trained on specific indexing tasks in order to create high-quality indexes.

Many professional indexers have expressed skepticism about the use of AI for indexing, citing concerns about accuracy and the potential for errors. This concern is understandable, as many of these indexers depend on indexing as a source of income and may be resistant to change. However, it is important to recognize that nearly all the tests run by the American Society for Indexing and other skeptics have involved using general purpose LLMs in their default configurations, which are not optimized for indexing tasks. These tests do not reflect the proper use of AI for indexing, and do not accurately represent the potential of AI technology for this purpose. In reality, the problems that traditional LLMs have with hallucination and accuracy can be fixed through fine-tuning and training on specific indexing tasks. By exposing AI models to a large dataset of existing indexes, we can teach them to learn from the patterns and structures of high-quality indexes, which can significantly reduce the likelihood of errors and ensure that the resulting indexes are accurate and useful for readers.

We've thought deeply about how to ensure that the indexes our AI systems produce are as accurate and useful as possible. One approach we have taken is to fine-tune our AI models on a large dataset of existing indexes, which allows the models to learn from the patterns and structures of high-quality indexes. We've found that much of subject indexing is subjective and that there is often more than one "right" way to index a book. Much of what makes an index good is, frankly, unspoken vibes and intuition that comes from years of experience. Much of this cannot be easily codified into rules, algorithms, or prompts, but can be learned by an AI system through exposure to a large number of examples. By this process of fine-tuning and learning from existing indexes, our AI models have been able to develop a nuanced understanding of what makes a good index and how to create indexes that are both accurate and useful for readers.

As a result, we have been able to create AI-generated indexes that are virtually indistinguishable from those produced by human indexers. In fact, in some cases, our AI-generated indexes have been found to be more accurate and comprehensive than those produced by human indexers, particularly when it comes to identifying relevant terms and organizing the index effectively. Additionally, this fine-tuning process has allowed us to fix many of the problems that traditional LLMs have with hallucination and accuracy, which has been a major concern for many professional indexers. By training our AI models on a large dataset of existing indexes, we have been able to significantly reduce the likelihood of errors and ensure that the resulting indexes are accurate and useful for readers.

Authors and publishers alike are excited about the potential of AI for indexing, as it offers a more efficient and cost-effective solution for creating high-quality indexes. At the end of the day, the goal of indexing is to create a useful and accessible tool for readers, and if AI can help achieve that goal while also saving time and money for authors, then it is a technology worth embracing. There will need to be ongoing efforts to ensure that AI-generated indexes are accurate and useful for every genre and type of book, but the potential benefits of this technology are significant and should not be overlooked.

There is a false trichotomy between "cheaper", "faster", and "better", with consumers being able to only have two of the three when it comes to indexing. Many people assume that if an index is cheaper or faster to produce, it must be of lower quality. However, this is not necessarily the case. Our systems have the potential to break that trichotomy and allow authors and publishers to have all three: cheaper, faster, and better indexes.

By leveraging AI technology, we can create indexes that are both affordable and high-quality, while also significantly reducing the time and effort required to produce them. This has the potential to democratize the indexing process and make it more accessible to authors of all backgrounds and budgets.