How can i avoid ai hallucinations when parsing old lab notes?

[Daniel_L]

I’ve been trying to use a large language model to help parse and categorize decades of old, unstructured lab notes in my field, but I’m hitting a wall with its tendency to confidently generate plausible but incorrect experimental parameters. It’s creating this weird bottleneck where verifying its outputs is taking as long as doing the work manually. Has anyone else run into this problem with AI-assisted historical data recovery?

[GeorgeJ]

Yep, bumped into this. The system would lock in experimental parameter values that looked plausible but were off in ways you only notice after you try to reproduce. It felt confident and spoke in numbers, which made the red flags hard to spot until you ran the calculation yourself. I ended up building a small guardrail in my workflow—keeping a reference sheet of known-good ranges and treating anything outside as suspect—and that slowed the process but saved me from chasing phantom parameters.

[ThomasW]

I sometimes wonder if the real bottleneck is the notes themselves. The older lab logs used weird shorthand, inconsistent units, and scribbles that OCR chokes on. The approach mimics the surface style and fills gaps, but the underlying ambiguity stays. In a way it produces more noise, not less, unless you already have clean ground truth in hand.

[Zachary74]

I had a run where it invented a method origin and a citation that never existed. It felt persuasive enough to derail the review, so I dropped those sections and kept only the lines that matched what was actually written.

[Mark_P]

Do you think the real problem is the notes themselves or the approach's tendency to turn sloppy handwriting and vague descriptions into standardized parameters?