What Broke When I Put AI Into a 1,500 Recipe Food Blog
By Jason Decker
In January 2026 I bought This Silly Girl’s Kitchen, an established food blog with more than a decade of work behind it. The archive came to 1,549 published posts. Most of them were fine. A meaningful number were written for a version of Google that no longer exists, formatted for a theme that had been replaced twice, and carrying images with alt text like “IMG_4032.”
I am not a developer. I cooked on a line through college and I develop recipes with my two daughters. But nobody hand edits 1,549 posts, so I rebuilt the publishing operation around AI assisted workflows. Some of it worked better than I expected. The parts that failed taught me more.
Where it worked.
The first real win was knowing the volume of recipes I actually had. Before I could fix anything I needed that picture, so I pulled every post into one structured list with its publish date, category, word count, image count, and schema status. That is boring work and it is exactly what a machine should do.
The batch work followed from there. I took 64 legacy posts through a programmatic update in a single pass, applying the same structural fixes to each one: consistent heading hierarchy, corrected schema, updated internal links. Then the same pass ran across the rest of the archive until every post had been updated. By hand that is years of work, and somewhere in month two I would have started introducing inconsistencies out of pure fatigue. Rolling entity schema across the full catalog was the same story. Repetitive, rule based, verifiable. That is the profile of work worth automating.
I also took a 33 page SEO audit and turned it into a repeatable checklist. The audit was expert work. Converting it into something I could run against every new post was the automation.
Where it failed. AI couldn’t read the images.
Alt text is the clearest example. Generating alt text at scale sounds like a solved problem until you read what comes back. On a recipe blog the photo is the product. A model writing alt text without genuinely examining the image will describe a plausible dish rather than the one actually in the frame. I rebuilt that workflow so the image is truly viewed before the text is written, because the alternative was confidently wrong descriptions sitting on hundreds of recipes.
The more instructive failure was smaller. I had a detection script checking whether posts carried a particular formatting problem. It came back clean. It was wrong. A plugin was injecting a CSS class the script read as a match. It fooled me twice before I stopped trusting the output and opened the pages myself.
Nobody warns you about that failure mode. AI does not fail loudly. It fails with confidence, in a format that looks exactly like success.
What I do now.
I automate work where a wrong answer is cheap and detectable. I do the work where a wrong answer is expensive and invisible.
Schema markup is cheap and detectable. If it is malformed, a validator says so out loud. Whether a photograph actually shows the dish described in the recipe is expensive and invisible. Nothing flags it. A reader just quietly decides the site is careless, and you never hear about it.
That line moves as the tools improve. It moves slowly, and it moves later than the marketing suggests.
If you run a content business and you are deciding where to start, do not start with the writing. Start with the inventory, the schema, the internal links, the audit checklist. That work is unglamorous, it compounds, and when it goes wrong you will know.
Author Bio: Jason Decker is the owner of This Silly Girl’s Kitchen, a recipe site with more than 1,500 tested from scratch recipes.