I've been using LLM a lot lately, kind of figuring out the use cases where it's really helpful vs where it falls flat. I recently had zigbee2mqtt kind of randomly fall over, maybe after a software update that I don't remember casually accepting, whereby I kept getting a failure loop in my logs. There was a conspicuous info message in the logs to check the guide for possible solutions, but I wanted to see if ChatGPT (4o model) was smart enough yet to figure it out.
The results were a mixed bag. ChatGPT was really clever about debugging what USB devices were seen by the system, using lsof to see if any other process had it opened, etc. For the most part, it read the logs well.
Except it totally ignored the part of the error message that recommended a possible solution. Kudos to the zigbee2mqtt devs for putting the link to the likely solution right there in the log!
I went back and forth with ChatGPT a bit to see if it might eventually come to the correct conclusion and it never really did.
It gets me to thinking... should we be thinking about logging from the perspective of (optionally) labeling things in a more AI-friendly format? So like right now the default looks like a typical syslog with a date & time stamp, loglevel, and then some strings of text. When I think of the big enterprise log management platforms like Splunk and Datadog, there's usually a bit more lifting to format log messages in such a way that the respective platforms can make more sense of the context of each log event.
While the Nabu Casa folks are figuring out where local LLM like ollama can fit into our ecosystem (something I'm playing a lot with right now myself), I think there's likely a lot of potential benefit to having Home Assistant logs labeled for something like ollama to consume, sending webhooks to an assistant to decide how to handle events of special interest.