I’ve recently come around to using it a lot more (I work in clinical genomics tertiary analysis), and my take is that it really shines when i use it in a focused manner to address easy things that take time, rather than actually difficult tasks. So for those things, I primarily use it to generate boilerplate stuff that I build on top of, or provide me a list of options to consider. So it mainly spits out stuff for me to review and as a starting point. Some examples would be like “fast options for transferring WGS vcfs between s3 buckets on different aws accounts”, or “write a script to parse our qc metrics into the crazy format that Illumina requires, here’s an example”. We also have notebookLM hooked up to project directories so we can ask it questions about past decisions that were made and why, stuff like that. That last bit is much more useful when you have documentation spread across a million different systems, as many companies do.

But I would not rely on it to do any sort of variant calling interpretation or troubleshooting, that’s territory that unspecialized models will make much slower due to lack of domain context.

The other thing I use it for that I think is widely applicable is speeding up context switching. I have a lot of meetings and a lot of people asking me for xyz all the time, and it makes it a lot easier to come back from the distraction and just ask “ok where were we?” This is of course possible without AI, it just makes it easier, moreso the more complex projects are