[vadman97]

Regarding model adaptation, we haven't yet explored a fine-tuned model, but it makes a lot of sense for a given class of errors. For a given code-base that is using highlight, the errors will typically be of a given language / infrastructure, so fine-tuning the model to those errors should be beneficial.

As for predicting potential errors, this will particularly make sense as an anomaly detection mechanism across metrics and logs. A class of 'important' errors based on the LLM's understand of the error, as well as historical comparison to normal error rates, is something we're exploring with an 'interesting errors' concept - stay tuned for more there!