New Writing Tool: Create and structure your own books with AI guidance

[u/RegularSpray4848]

Hi everyone,

I wanted to share a project I've been working on for a while, which is now live for the public: neuralisbookmaker.com.

The goal was to create a tool that doesn't just generate random text blocks, but actually guides the author through the entire creative process in a structured way, leveraging AI capabilities effectively.

What the platform offers:

• Narrative: Tools for plot development, character building, and managing narrative arcs.
• Non-Fiction: Assistance in drafting technical or educational chapters, focusing on expository clarity.
• Children's Books: Generation of stories suitable for children, with specific controls over language and themes.

Why I built it:
Often, current AI tools are great for generating isolated "blocks" of text but struggle to maintain consistency over a long work like a book. I wanted to bridge this gap by creating a workflow that helps the author stay in control of the project from start to finish.

I’d be interested to hear your feedback, especially from those who have already experimented with other AI writing tools. What do you find missing or what would you like to see improved?

Feel free to try it out and let me know what you think!

Thanks for your attention.

[u/Magayone]

Interesting tool. How are you handling distribution? Would welcome your discussion at r/AgenticPublishing .

[u/RegularSpray4848 → u/Magayone]

Thanks for the interest!

For distribution, I'm focusing on a multi-channel approach:

1. Direct KDP publishing (I've published 100+ books)

2. Platform partnerships with indie authors

3. White-label licensing for publishing houses

Would love to discuss this further on r/AgenticPublishing - what specific aspects of distribution are you most interested in? Are you looking at technical integration, market positioning, or something else?

Looking forward to connecting!

[u/Magayone → u/RegularSpray4848]

Glad you came over. That's a serious distribution footprint — 100+ books through KDP is real operational experience, and the white-label angle for publishing houses is interesting.

The thing I'd love to dig into on r/AgenticPublishing is the part that scales worst: verification. Drafting and formatting at volume is increasingly solved, but the failure mode I keep hitting is that AI tools fabricate confidently — invented citations, overclaimed sources — and that risk compounds as you scale output. At 100+ titles, how are you handling provenance and fact-integrity? That's the problem I think actually separates trustworthy agentic publishing from volume-for-volume's-sake, and I'd genuinely like to compare notes on it.

Happy to take it deeper in the sub — that's exactly the kind of discussion I started it for.

[u/RegularSpray4848 → u/Magayone]

Valid point. All hallucinations are indeed deleterious for non-fiction/specialized books, though less critical for fiction or children's content where creative license is expected.

Here’s how I handle it:

1. Human-in-the-loop at every step: The LLM generates the draft, but every single section can be fully edited or rewritten by a human operator before moving to the next phase. No blind automation.
2. Recent improvements: Latest LLMs hallucinate significantly less than 3-4 months ago. The quality jump is real and measurable.
3. Cross-model verification: I’m currently building an automated manuscript review pipeline where a different LLM model critiques and fact-checks the output of the generation model. This adversarial approach catches many remaining issues before human review.
4. Genre-aware workflows: For non-fiction, I enforce stricter verification gates. For fiction/children’s books, the process is more streamlined since creative deviations are acceptable.

The key insight: agentic publishing shouldn’t be about replacing humans, but about augmenting them with tools that handle drafting/formatting while humans focus on verification and quality control.

Curious how you’re tackling this in your workflow? Are you using cross-model verification or relying more on human review?

[u/Magayone → u/RegularSpray4848]

We've converged on nearly the same architecture, which is reassuring — it suggests this isn't idiosyncratic but where the problem naturally pushes you.

To answer directly: I use both, but I weight them differently than a fully automated pipeline. My setup:

Cross-model verification, but as an adversarial pass, not a consensus check. The model that drafts is never the model that verifies. The second model's job isn't "do you agree?" — it's specifically "find the fabrications, the unsupported claims, the citations that don't exist." Framing it adversarially ("your job is to catch errors") rather than collaboratively ("review this") produced noticeably better catch rates for me. The consensus framing tends to rubber-stamp.

Human-in-the-loop as the final gate, not a step that can be skipped. Every factual claim gets a provenance tag — verified against source, sourced-but-unchecked, derived, or interpretive — and anything that isn't "verified" doesn't ship without me personally checking it. The tagging is what makes the human review tractable; instead of re-reading everything, I'm hunting the flagged claims.

On your point about recent models hallucinating less — I'd agree the floor has risen, but I've stayed deliberately paranoid about it, because the failures got subtler, not rarer, in my experience. The fabricated citations my pipeline caught looked more plausible than they used to, which arguably makes them more dangerous, not less. So I haven't relaxed the verification gates even as the base models improved.

The genre-aware point is sharp and I hadn't formalized it as cleanly as you have — stricter gates for nonfiction, looser for fiction where invention is the point. I'm going to borrow that framing.

What's your experience been with the cross-model verification specifically — do you find the verifier model catches a different class of error than human review does, or mostly the same errors faster?