We have an ongoing effort in parsing logs for our autotests to speed up debug. It is vary hard to do, mainly because there is a metric ton of false positives or plain old noise even in the info logs. Tracing the culprit can be also tricky, since an error in container A can be caused by the actual failure in the container B which may in turn depend on something entirely else, including hardware problems.

Basically a surefire way to train LLM to parse logs and detect real issues almost entirely depends on the readability and precision of logging. And if logging is good enough then humans can do debug faster and more reliable too :) . Unfortunately people reading logs and people coding them are almost not intersecting in practice and so the issue remains.