How can I get a language model to generate insights from messy lab notes?

[Edward82]

I’ve been trying to use a large language model to help categorize and generate hypotheses from my messy lab notes, but I’m hitting a wall where its suggestions feel generic and miss the subtle connections I see. Has anyone else found that the model's statistical pattern recognition just can't replicate the intuitive leap a human researcher makes from disparate data points?

[Hannah_J]

Yeah I’ve bumped into that with the model. The notes look like chaos and the suggestions feel generic, like you could slot anything in and get similar clusters.

[LaylaVG]

In my case I tried cleaning up the data a bit and tightening the prompts, but still the outputs felt shallow and obvious.

[JohnXM]

Maybe the real blocker is the data quality or missing context rather than the tool; a few key observations just aren’t in the notes.

[OliverL]

I tried giving it explicit hypotheses and a rough scoring idea, and the results were inconsistent across runs, which was annoying.

[ElizabethFT]

One time I drifted off topic about calibration curves and came back to the main thread, only to realize the core pattern was still loose, then got distracted again.

[ChloeL]

Do you think layering a human in the loop would help, or is that just adding friction?