How can i bridge broad ai knowledge with domain-specific reasoning in lab notes?

[Isabella74]

I’ve been trying to use a large language model to help categorize and generate hypotheses from my messy lab notes, but I keep hitting a wall where its suggestions feel generic and miss the subtle connections I see. It’s like the model lacks the specific context of my experimental setup, even with fine-tuning on my own documents. Has anyone else found a way to bridge that gap between broad AI knowledge and deep, domain-specific scientific reasoning?

[Benjamin23]

I have been there I found a small scaffold helped more than changes to the model I added a few labels from my notes like variable instrument and time and I dropped in a couple of example hypotheses found in old experiments It still feels like it offers generic ideas but at least it ties back to something concrete from my data

[GregoryR]

I tried fine tuning on my lab notes but I hit overfitting and the model echoed phrases from the training set while missing the quirks of my setup We kept a human in the loop a researcher would review the top ideas prune the wrong ones and we used those edits to steer prompts next time The gains were small and delicate and depended on a single snippet

[Zachary_S]

It might be the notes themselves If the language is all over the place the model can only guess I started a tiny normalization pass up front mapping synonyms to a shared term and trimming stray shorthand Then I asked the model for hypotheses after that steering The improvement was small but real and it kept the model from chasing connections that only lived in the jargon

[Stella_R]

It helps to also check with a simple graph of how ideas relate or a gut feel of what makes sense I drifted into thinking the problem might be bigger than the tool like maybe data quality is the real bottleneck Do you think the real bottleneck is how we structure the prompt