Can AI identify 'narrative gaps' or missing content essential for a complete nonfiction argument, and how does it suggest solutions?

Question

Tyler Smith · Accepted Answer

Yes, AI is becoming highly adept at identifying 'narrative gaps' in nonfiction manuscripts – instances where a logical step is missing, an argument lacks sufficient support, or a transition feels abrupt. This capability is rooted in its ability to build an 'Internal Model' of not just the existing content, but also the expected logical flow and evidentiary requirements for a given genre or argument type. Similar to how *Risk-First Software Development* advocates for identifying 'Attendant Risks,' AI first analyzes the explicit arguments and claims made by the author.

Then, it cross-references this against common argumentative structures and knowledge bases relevant to the book's topic. For example, if an author presents a problem and then jumps to a solution without adequately detailing the impact of the problem or exploring alternative solutions, the AI can flag this as a potential gap. It might suggest, "Consider adding a section on the 'socio-economic impact of X' to strengthen the foundation for your proposed solution Y." It can also identify where a claim lacks supporting evidence by comparing it against a database of verified information or by noting the absence of citations in a section where they would typically be expected. The AI doesn't just point out omissions; it can also suggest types of content that would fill these gaps, proposing research avenues, rhetorical devices, or additional examples needed to complete the argument. This proactive identification helps authors and co-authors address structural weaknesses before they become significant issues in later stages of the book lifecycle.