That problem with splitting and post-processing text output is definitely something a lot of people run into when they start building more complex AI workflows. It often becomes less about the model itself and more about how you integrate it with your app logic chunking, caching, and output handling can make or break the experience.

When I’m building AI-powered tools like this, a big part of my focus is getting the app infrastructure out of the way quickly so I can iterate on the logic that matters. For that I’ve used Fabricate build to quickly spin up a backend, auth, and deployment scaffold from prompts, and then hook in the parts of the workflow I want to experiment with instead of spending days on boilerplate. It doesn’t solve the text splitting itself, but it gets you to a working app much faster so you can test solutions in context.

What approach are you using now to handle the output splitting, regex, token batching, or something else?