April 2026
Intermediate
592 pages
13h 28m
Korean
Content preview from 에이전트 시대의 AI 시스템 설계
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538
에이전트 시대의 AI 시스템 설계
이미지가 얼룩졌다면 그럴 수 있겠다 싶지만, 입력이 깨끗하더라도 환각이 발생한다는 것이 문
제이다. 집필 시점을 기준으로 이미지에서 추출된 필드의 정확도는
90
%에서
97
% 수준이다.
이는 청구서나 영수증 등에서 추출된 숫자의
3
%에서
10
%가 환각이라는 뜻이다! 게다가 추출
된 데이터를 다른
LLM
호출의 입력 컨텍스트로 넘기면, 오류 감지나 수정 기능이 있어도 오류
발생 가능성이 눈덩이처럼 불어난다.
LLM
호출 체인이 복잡할수록 응답이 환각일 가능성은 더
커진다.
LLM
이 환각을 일으키는지 어떻게 판단할까? 서로 겹치지 않는 데이터셋으로 훈련된 아주 다
른
LLM
세 개가 있다고 하자. 이 세 모델로 응답을 생성해 비교했을 때 서로 다르다면 환각일
가능성이 높다. 정답이 뭔지는 몰라도, 적어도 잠재적인 문제가 있음은 알 수 있다.
LLM
응답
의 가변성을 이용해 환각이 의심되는 지점을 직관적으로 찾아낼 수 있는 것이다.
하지만 프런티어 모델
frontier
model
들은 훈련 데이터셋이 상당히 많이 겹치기 때문에, 데이터가 겹
치지 않는
LLM
세 개를 구하기는 어렵다. 게다가 여러
LLM
으로 인퍼런스하면 비용도 배로 든
다.
LLM
하나의 응답만 보고 잠재적인 환각 지점을 찾아낼 방법은 없을까?
9.3.2
해법
제
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and much more.
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.