What can fix flaky results from llms when parsing lab notes?

[James_M]

I’ve been trying to use a large language model to help parse and categorize decades of unstructured lab notes in my field, but I’m hitting a wall with its consistency. It will correctly identify a complex experimental protocol one moment, then completely misinterpret a standard chemical notation the next. This unpredictability makes me hesitant to rely on it for anything systematic. Has anyone else dealt with this kind of erratic performance in a structured research task?

[Sofia35]

Yeah, I ran into that too. It can nail a protocol one day and read a chemical symbol completely wrong the next. It feels like you’re chasing a moving target.

[Savannah.W]

In my case I tried batching notes into weekly chunks and asked it to categorize by experiment type. Some days it felt confident, other days it produced goofy misreads that threw off the whole category.

[RobertVG]

I did a tiny pilot where I labeled a few hundred notes by hand and then compared the model output. The gap opened up on OCR’d scans and handwritten margins, which is exactly where mistakes crept in.

[John_T]

Maybe the real hurdle isn’t the tech but the notes themselves. If terms drift or labs switch conventions, any parser will wobble.

[Emily.W]

I felt speed was the enemy. When I pushed to process large batches, the output got brittle fast and I stopped using it for anything near live data.

[HarperJ]

One time I thought a specific notation was standardized in the field, only to find out a collaborator used a different shorthand that the model hadn’t seen, so it spun a few false positives.

[Donald_J]

Have you tried starting with a single well defined subset and sanity checking it with a human before expanding?