Beyond developmental editing, how does AI personalize narrative structure and examples to resonate with diverse nonfiction reader demographics?

Question

Tyler Smith · Accepted Answer

While developmental editing focuses on core structure and argument, Clove's AI goes further by enabling a nuanced personalization of narrative to resonate with diverse reader demographics. This process involves more than just swapping out a few words; it entails adapting the 'Internal Model' of the book to specific audience 'Goals,' much like Robert Moffat’s approach to risk in software development. For example, if a nonfiction book on climate change is intended for both a scientific community and a general policy-maker audience, the AI can suggest alternative framing for certain sections.

It analyzes the target demographic's known preferences for data presentation (e.g., more technical graphs for scientists, plain language summaries for policy-makers), relevant case studies, and even cultural sensitivities. The AI identifies areas where 'Attendant Risks' like alienating a segment of the audience through overly technical jargon, or 'Hidden Risks' like using an example that might not resonate globally, could occur. It proposes adjustments to examples, analogies, and narrative emphasis to better align with the specific audience's prior knowledge and interests, ensuring that the critical message remains intact while its delivery is optimized for maximum impact. This allows authors to effectively segment and re-package their content for different markets without having to write entirely new books from scratch, maximizing reach and engagement.