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지난 7월에는 한 예능프로그램에서 예능인 김준현이 촬영 중 소액결제 사기를 당해 수백만원의 피해를 입는 모습이 그대로 방송되기도 했었다. 낚시를 하던 김준현은 갑자기 휴인천화 소액결제 알림이 끊임없이 울리자 당황하며 좁은 배 뒤에서 갈팡질팡했다. 방송에서 김준현은 “누가 게임으로 3600원씩 200건을 썼는데 63만원을 초과했었다. 문자가 한 번에 800개나 와 있습니다.
Relying on a single AI model is a gamble. Mixing models helps manage unique strengths and failure modes, but synthesis can mask critical dissent or leak data across providers. It adds complexity that is a nightmare to debug
Why mix LLMs? It balances strengths and catches unique failure modes. Yet, collapsing outputs often hides critical dissent and risks leaking data across provider boundaries
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Everyone wants a silver bullet, but relying on a single LLM is a recipe for silent failure. We use multi-model setups to hedge against individual model weaknesses and exploit specialized strengths. However, blindly synthesizing outputs is risky
Why chain models? Sure, mixing them exploits specific strengths and checks for errors. But stitching systems creates new failure modes. Watch out—synthesis can hide dissent or leak data across providers. Troubleshooting broken chains is a nightmare.
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Routing between models helps you hedge against specific failure modes and leverage unique strengths. Just watch out—synthesis can bury critical dissent or leak data across providers. Track your costs and logic. Don’t trust black boxes blindly.
Relying on a single model is risky. Using several helps manage distinct failure modes and catch hallucinations via disagreement. But be careful. Automating synthesis often hides vital dissent or leaks data. It is a tool for reliability, not a magic fix