What I learned building an AI book publisher<br>>Ted Benson/WritingTinkeringAbout

What I learned building an AI book publisher

This blog is 100% human authored. For AI-authored writing, see Robot Book Club.<br>17 July 2026<br>For the past few months, I’ve been hacking on a side project to generate print-quality cookbooks. At the start of this project, I set three hard requirements for myself:

Generation Input should be no more than what would fit on a book cover

Generation Output shouldn’t be slop — it should be good enough to justify printing.

Generation must be end-to-end automated

So far, that’s produced 53 cookbooks over more than 500 attempts consuming hundreds of millions of tokens.<br>All free on the web and as PDF & EPUB downloads.

The books aren't yet worth printing, in my opinion.<br>But the recipes, and recipe curation, are a pretty darn good, to the point I’d be wiling to defend the “no slop” claim.

I’m currently on a plane over the Pacific, unable to check in with my robot authors, and honestly a bit hungry. So I figured what better time to jot down a few notes about what I’ve learned so far.

In case it’s important to you: while this post is about AI writing, it's written and edited without AI.

First: Why cookbooks?

I love to cook, but I chose to generate cookbooks because I think it's a bit of a cheat.<br>Cookbooks are as close as you can get to a type of long-from content LLMs can generate well, autonomously, at this point in their development cycle.

First, Cookbooks are episodic. LLMs struggle to maintain humanlike pace and cohesion across long-form text generation. But cookbooks minimize these issues because there are few cross-book dependencies to keep track of. It’s like generating a sitcom episode compared to the Game of Thrones finale. Cross-chapter and cross-recipe dependencies are mostly relevant distributionally — avoiding overuse of the same trope over and over throughout the book.

Second Cookbooks are mostly an act of curation and translation , and LLMs are great curators and translators. Writing the next great novel requires layers of creative spark and balance, But even human cookbooks are essentially a curation of existing cultural knowledge, translated into a particular chef’s take on the cuisine. Chili, but vegan. Pizza, but with pesto. Pho, but with American ingredients.

So cookbooks, to me, are akin to the easiest long-form writing eval that you could create for an LLM.

How does it work?

Robot Book Club is organized into different series of books.<br>Each book series is a program that produces a finished book based on a few simple inputs: the book title, the back-cover description, and a few notes on author voice.

Right now there are three cookbook series:

The Diaspora Series , which contains recipes second generation Americans might learn from their grandparents. (Examples: Japan, Cuba, Spain)

The At Home Series , which adapts famous restaurant styles into home cooking. (Examples: Jewish Deli, Chinese Takeout ..)

The Diet Series , which contains recipes for people who maintain specific diets (Examples: Whole 30 Quick Meals)

I hand wrote the very first book series program — The Diaspora Series - through a lot of trial and error. From that series, I extracted an agent skill, and every series program since has been AI-authored through the use of that skill.

While publication goes straight from generation onto website, I do skim through a random selection of book PDFs afterward. This produces a list of change requests, which I feed back into my agent to update both the series program skill and also the existing programs. You can see how regenerations of each book have happened based on the “Edition Number” on the website.<br>Taiwanese is on Edition 13 — though the real number is probably something like 200 since I hand-wrote that one — while some of the other books like Vietnamese are only on Edition 3, since they were generated using the most recent version of the program.

Each book series program is a workflow of agents rather than an agent itself. I might just be resisting our agentic futures, but I’m not yet sold on the idea that the top-level control plane should be yielded to an LLM, at least for something like Robot Book Club. Each series has a very specific research, writing, editing, typesetting, and publishing process that I don't want to veer from. That process includes many, many agents within it, but the overall process itself should, I think, remain rigid.

Having that top-level rigidity also supports a more controlled error handling, error recovery, logging, and observability setup — something that’s really helped scale the output volume.

Workflows help you scale output quality

Workflows have been the biggest factor in scaling quality. I don’t mean Anthropic’s branded Workflow feature, but the generalized idea it represents: encapsulating common patterns of LLM processing and then using them over and over again via a simple invocation. A bit like functional...