feat: 피드백 상세화 — 면접관별 상세평가·점수근거 + 종합 서술형 + 학습 방향#116
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한 줄 강점/약점·기계적 요약이던 피드백을 두껍게. AI: - 평가위원 출력에 detail(근거·예시 포함 2~4문장)·score_rationale 추가(프롬프트) - 종합 synthesis 체인 신설(feedback_synthesis): 패널 결과를 통합해 서술형 강점/약점 문단 + 개선 키워드 + 학습 방향(study_plan) 생성. 실패 시 기계적 병합 폴백 - PanelFeedbackGenerator 에 synthesis 연결, 콜백에 study_plan/detail/근거 전달 Core: - session_feedbacks.study_plan jsonb(V17), PanelBreakdownItem 에 detail/scoreRationale, 콜백 수신·저장·FeedbackResponse 노출(공개 공유 포함). openapi 재생성 Frontend: - 피드백 리포트: 면접관별 상세평가·점수근거 표시 + "학습 방향" 섹션 검증: AI 251 / 백엔드 BUILD OK(분해·study_plan 저장 테스트) / 프론트 tsc·eslint·vitest 55. 비용: 피드백당 LLM콜 = (직군 수+2) 평가위원 + synthesis 1. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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한 줄 강점/약점·기계적 요약이던 피드백을 두껍게.
AI:
Core:
Frontend:
검증: AI 251 / 백엔드 BUILD OK(분해·study_plan 저장 테스트) / 프론트 tsc·eslint·vitest 55.
비용: 피드백당 LLM콜 = (직군 수+2) 평가위원 + synthesis 1.
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