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Reduce LLM Hallucinations? Why 'Make-No-Mistakes' Fails
How-ToMachine Learning

Reduce LLM Hallucinations? Why 'Make-No-Mistakes' Fails

via Dev.toSimon Paxton2h ago

The first time you see it, it’s kind of perfect: a tiny folder in your Cursor skills called make-no-mistakes . One more tool in the drawer, one more checkbox ticked. You install it, feel a small wash of relief. Finally—something to reduce LLM hallucinations without re‑architecting your whole stack. The README plays along. “Mathematically rigorous.” “Zero mistakes.” A “claimed 0.067% performance boost (18th shot, temperature 0.0).” The joke is loud enough to hear, but the desire underneath it is quieter and more honest: please, let there be one file I can drop into skills/ that makes this all safe. That’s the interesting part. Not the repo itself, but what it reveals. We don’t just want models that hallucinate less. We want the feeling that someone else has already done the hard thinking for us—and wrapped it in a single skill. TL;DR You cannot reduce LLM hallucinations to zero with a one‑line skill; pretending you can is performative safety that actively increases risk. Real gains come

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