Yes. The difference is you can immediately benchmark and iterate via prompting without having to retrain. It's such a time saver.
Is it though? I have found it much easier to fit/diagnose/improve simple classifiers on embeddings/hash vectorized features/etc than iterate on prompts, especially true when using the more modern LLM's (like gpt 5.6 Luna) where you can't even set the temperature to get any sense of determinism.
I do note though, that is infinitely easier to 'deploy' a Jev/LLM based solution than a data/model pipeline.
The comparison i'm making is against a training of encoder/small decoder model so I think we agree on most things. I dont think jev replaces the benefit of embeddings nor think they are mutually exclusive. All part of a handy utility belt.