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

Hacker News Discussion ↗

Large language models are increasingly being treated as authoritative sources, even when they confidently produce wrong or fabricated information, and many people are alarmed by how quickly users suspend critical thinking. Commenters compare this to earlier waves of misplaced trust in search results, social media, or traditional media, but note that LLMs feel more persuasive because they hide their sources and speak in fluent, human-like prose. Suggested responses range from gently re-framing LLMs as fallible tools and demonstrating their errors, to emphasizing user responsibility and better education about evidence and sources, while some argue that for many low-stakes queries they’re already no worse—and sometimes better—than today’s degraded web.

Scope of the Concern

  • Many see LLM overtrust as a continuation of people uncritically believing search results, social media, or news — just faster and slicker.
  • Others think it’s worse: LLMs hide sources, sound confident, and mimic human conversation, short‑circuiting skepticism even in otherwise logical people.

Why People Trust LLMs Too Much

  • Human‑like dialogue and technical tone make outputs feel authoritative.
  • Some anthropomorphize models (talking about them as sentient, oracles, “personal Jesus,” or deities).
  • Users often seek confirmation, not disproof; LLMs are good at supplying plausible confirmation.
  • Many lack a solid grasp of what “truth,” evidence, or “reputable source” actually mean.

Failure Modes & Harms

  • Hallucinations: fabricated legal cases, bogus rehab plans, wrong technical advice, and confident nonsense.
  • Sycophancy: some models readily change answers to agree with the user; others push back more.
  • Prompt‑sensitivity: slightly rephrased questions (“why X is good” vs “why X is bad”) yield opposite‑framed answers.
  • Outputs are structurally valid and fluent even when factually garbage, so errors propagate unnoticed.

How Commenters Deal with Overtrust

  • Gentle reframing: insist on saying “it,” describe LLMs as tools like calculators, show contradictions or 180° reversals in the same session.
  • Ask “Where did that come from?” and push for primary or clearly identified sources.
  • Demonstrate failures live (self‑contradictions, obvious hallucinations, jailbroken models, r/aifails).
  • Hold users responsible: treat LLM output like any other source; if they use bad info, it’s still on them.
  • In high‑stakes cases (medical, legal, policy): urge deference to experts and original research.
  • Some simply disengage or ignore “AI‑psychosis” types; others see it as an education problem that will take time.

Norms for “Good” Use

  • Use LLMs as a first pass, summary, or search UI, then verify with primary / human‑curated sources.
  • Ask for citations, then manually check them (or use multiple models/agents to cross‑check).
  • Treat LLMs like a junior coworker: potentially useful, never unreviewed.