April 2026
Intermediate
592 pages
13h 28m
Korean
Content preview from 에이전트 시대의 AI 시스템 설계
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249
4
장
지식 추가: 고급
도움이 되지만, 그 효과는 검색된 문서 청크의 유관성에 크게 의존한다. 검색 단계에서 쿼리와
는 관련이 없는 문서 청크나 부정확한 정보를 담은 청크가 검색된다면 문제가 될 수 있다.
사용자 쿼리
응답
두 출처 모두 결합
문서 분해 후 재구성
응답 생성
웹 검색
낮음
높음
모호함
문서 사용
검색기
검색 평가기
문서 품질?
그림
4-10
CRAG
작업흐름
교정
RAG
혹은
교정 검색 증강 생성
corrective
retrieval
-
augmented
generation
(
CRAG
)의 핵심 목표는
LLM
이 응답을 생성하기 전에 환각을 탐지하고 수정하는 것이다. 이 접근법은 평가기
evaluator
를 프로
세스에 추가한다. 응답 생성 단계로 문서 청크들을 넘기기 전에, 평가기가 주어진 쿼리에 대한
250
에이전트 시대의 AI 시스템 설계
그 청크들의 품질을 평가해서 확신도 점수
confidence
score
를 산정한다.
CRAG
파이프라인은 이 점
수에 따라 여러 가지 지식 검색 작업을 추가로 수행한다. 문서가 관련이 없거나 모호하다고 판
단된 경우 두 가지 전략 중 하나 또는 둘 다를 적용할 수 있다. 첫째는 웹이나 기업 문서 저장소
(셰어포인트
SharePoint
나 구글 드라이브 등 )를 검색해서 콘텐츠를 보강하는 것이다. 지식 베이스
는 다소 정적이지만 온라인
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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.
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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.