One example - a local model (Mistral 7B OpenChat 3.5, which ranks high in my benchmarks) was generating additional search keywords for products at online marketplace. A second run of the model was cleaning up bad keywords.
The fun part here - ChatGPT-4 reviewed some user searches, product details and comments of marketing department. Then it generated condensed tutorials on writing good keywords for the products in this system (keywords have to cover unexpected search terms that people would use when searching for a product).
The tutorial was supposed to be for the "junior marketing assistant", but in reality it was fed to Mistral 7B.
The second pass was done similarly. "Hey, ChatGPT, these are some sample keywords that are produced by junior marketing assistant according to your tutorial. Review them and write a short guide on correcting most common problems".
It works nicely.
Other cases of local models that work good - custom embeddings (multi-lingual, mapped to the same vector space), custom TTS, custom STT. These are mostly used for specialised personal assistants.
