What pitfalls should I watch for when using a language model on messy lab notes?

[Camila.H]

I’ve been trying to use a large language model to help categorize and generate hypotheses from my messy lab notes, but I’m finding its interpretations of my shorthand and technical terms are often way off. I’m worried this is introducing errors I might not catch, especially when it makes a plausible-sounding but incorrect inference about a procedure. Has anyone else run into this problem with automated research assistance?

[EleanorM]

Yeah, I’ve been there. My notes are a tangle of abbreviations and stray shorthand. The model would latch onto a plausible inference and I’d only notice it after I checked against the real protocol. I started a tiny glossary mapping each shorthand to a canonical term and I pre-cleaned the notes before feeding them in. It reduced some errors, but it never fully solved the misreadings.

[DanielL]

I wonder if the issue isn’t the model’s training but input quality. When I feed messy text into an LLM, it fills in gaps with likely-sounding steps, which feels risky in a lab context. It’s tempting to treat it as just a helper, but the risk of a wrong hypothesis slipping through is real.

[PenelopeUG]

Have you tried a tighter workflow, like restricting vocabulary and only letting it propose hypotheses on a fixed set of variables?

[Joshua.J]

Sometimes I drift off topic and think maybe the problem is the notes themselves, not the model. The day I added a simple timestamp to each entry and kept a running log of what I actually did, I saw the model guess shift in subtle ways. It was a tiny signal, but it reminded me how context matters.