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LLM API 프로덕션 호출의 진짜 어려움, RAG가 단순 조회가 아닌 이유, 에이전트 패턴(ReAct/Plan-Execute/ReWOO), 파인튜닝 언제/언제 하지 말까(LoRA/QLoRA/DPO), 벡터 DB 선택 매트릭스, LLM 평가의 근본적 어려움, 비용 최적화, Prompt Injection 방어까지. 데모가 아닌 '진짜 프로덕션' AI 앱을 만드는 법.
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ReAct 프레임워크, Tool Use, 멀티에이전트 오케스트레이션, MCP(Model Context Protocol)부터 Computer-use 에이전트까지 2026년 Agentic AI 완전 가이드입니다.- Published on
챗봇의 멀티턴 대화에서 컨텍스트를 효과적으로 유지하는 메모리 관리 전략을 다룹니다. LangChain의 다양한 메모리 타입(Buffer, Summary, Window, Entity)과 LangGraph의 상태 기반 에이전트 아키텍처, 영속 메모리 구현, RAG 연동까지 프로덕션 환경에서의 실전 패턴을 설명합니다.- Published on
LLM 에이전트 프레임워크 3종(AutoGen, CrewAI, LangGraph) 종합 비교 가이드. 아키텍처 설계 철학, 멀티 에이전트 오케스트레이션 패턴, 도구 통합, 메모리 관리, 프로덕션 배포 전략, 그리고 실전 선택 기준까지 코드 예제와 함께 다룹니다.- Published on
LangGraph 기반 멀티 에이전트 오케스트레이션 패턴과 NeMo Guardrails를 활용한 안전장치 구현부터 프로덕션 배포, 운영 노하우까지 다루는 종합 가이드.- Published on
LangGraph 기반 멀티에이전트 챗봇의 그래프 설계부터 상태 관리, 도구 통합, 가드레일, 평가 체계, 프로덕션 배포까지 실전 가이드.- Published on
LangGraph로 상태 기반 AI 에이전트 워크플로우를 구축한다. StateGraph, 조건부 라우팅, 멀티에이전트 오케스트레이션, Human-in-the-Loop, 그리고 LangGraph Platform 배포까지 실전 코드 포함.- Published on
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