More than the individual tab completions, I've found that when working on a feature across many files, when I open the next file half the time I can just press tab a few times (and no other keys) to make all the edits I wanted in that file in a matter of seconds. That's what continues to blow my mind every time.
Right now we offer traditional fine tuning with prompt/completion pairs but not training a reward model. This works great for a lot of use cases including classification, extracting structured data, or responding with a very specific tone and style.
For making use of domain specific data we recommend using semantic search to pull in the correct context at runtime instead of trying to fine tune a model on the entire corpus of knowledge.
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