Ask HN: How do you deal with people who trust LLMs?

Are you saying there are data graphs that don't have humans in the chain? If so, what came up with the data and the tools to generate those graphs? And how do you decide which data and graph to trust? What exactly makes them "good sources"?

> If I had to choose between a map from Google maps

I would too. But Google Maps relies on local 3d party survey companies that use people, manual GIS tools, and image recognition AI. How do you know they don't have any mistakes in them? In fact, I live in a country where local area names are frequently misspelled on Google Maps, and reverse geocoding gives misleading addresses.

I feel my point that all these "reputable sources" or "good sources" have biases (and mistakes) still stands.

I must also point out that the 3 concrete examples given against my replies all involved visual content like graphs, maps, Peanuts cartoons, etc. But my comments were written with the typical text-based usage for QA in mind. I don't know if LLMs can fact-check map imagery or data graphs (probably not, but I've never tried). It's just not the kind of thing I'd ever use LLMs for, to begin with.

> Coffee consumption was more often associated with benefit than harm for a range of health outcomes across exposures including high versus low, any versus none, and one extra cup a day. There was evidence of a non-linear association between consumption and some outcomes, with summary estimates indicating largest relative risk reduction at intakes of three to four cups a day versus none, including all cause mortality (relative risk 0.83, 95% confidence interval 0.83 to 0.88), cardiovascular mortality (0.81, 0.72 to 0.90), and cardiovascular disease (0.85, 0.80 to 0.90). High versus low consumption was associated with an 18% lower risk of incident cancer (0.82, 0.74 to 0.89). Consumption was also associated with a lower risk of several specific cancers and neurological, metabolic, and liver conditions. Harmful associations were largely nullified by adequate adjustment for smoking, except in pregnancy, where high versus low/no consumption was associated with low birth weight (odds ratio 1.31, 95% confidence interval 1.03 to 1.67), preterm birth in the first (1.22, 1.00 to 1.49) and second (1.12, 1.02 to 1.22) trimester, and pregnancy loss (1.46, 1.06 to 1.99). There was also an association between coffee drinking and risk of fracture in women but not in men.

> Conclusion Coffee consumption seems generally safe within usual levels of intake, with summary estimates indicating largest risk reduction for various health outcomes at three to four cups a day, and more likely to benefit health than harm.

When I'm looking for medical advice, I want that advice to list things like "coffee drinking might not be safe during pregnancy".

Furthermore, the statement 'Heavy consumption (6+ cups) can lead to anxiety, insomnia ...' assumes caffeinated coffee, yes? The paper I linked to also discusses decaffeinated coffee, eg:

> High versus low intake of decaffeinated coffee was also associated with lower all cause mortality, with summary estimates indicating largest benefit at three cups a day (0.83, 0.85 to 0.89)28 in a non-linear dose-response analysis. ...

> Coffee consumption was consistently associated with a lower risk of Parkinson’s disease, even after adjustment for smoking, and across all categories of exposure.22 76 77 Decaffeinated coffee was associated with a lower risk of Parkinson’s disease, which did not reach significance. ...

> there were no convincing harmful associations between decaffeinated coffee and any health outcome.

That nuance seems important.

Also note that this paper is incomplete as it investigated defined health outcomes, not physiological outcomes like anxiety. There are plenty more papers, like https://academic.oup.com/eurheartj/article/46/8/749/7928425?... , which considers the time that people drink coffee, also discusses decaffeinated coffee, and highlights the uncertainty about the effect of heavy coffee drinking.

I don't see why I should care to ask an AI when it's so easy to find well-written research results which are far more likely to cover relevant edge cases.

Building an agent debugging tool taught me this concretely: LLMs will return structurally valid, fluent garbage mid-loop and the agent keeps running. No error. No warning. Just wrong output propagating forward silently.

The people who deal with LLMs best treat them like a junior dev who writes clean-looking code that hasn't been tested. You don't distrust everything they say — you just never skip the review step.

Like, "!w Peanuts" in my search bar, look at the image, and count fingers.

"a rather outlier example"

You wrote that you use AI to find "obscure connections" - aren't those all by definition outliers?

"mostly as text-based question"

I just now asked Google AI "how many fingers are on charlie brown's hand?"

It replied "In the Peanuts comic strip, Charlie Brown and the rest of the gang are traditionally drawn with four fingers (or three fingers and a thumb) on each hand."

No image comprehension, exactly as you had in mind. And completely false.

And that's from a training corpus which almost certainly includes statements that the kids are drawn with 5 fingers, since I confirmed that info on TVTropes and Reddit comments, like https://www.reddit.com/r/pics/comments/swod8/charlie_brown_h... .

I think it's a topic worthy of discussion. But I would propably not leave it to Searle...