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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.
Ai-engineering
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- Published on
LLM을 프로토타입에서 프로덕션으로 가져가는 여정은 Jupyter 노트북의 데모와 다르다. RAG 파이프라인 설계, eval harness 구축, fine-tuning 결정 트리, LLMOps 관측 가능성, Guardrails와 안전성, Prompt Caching과 비용 최적화까지 2025-2026년 AI 엔지니어의 현장 가이드를 500줄로 정리한다.- Published on
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Python은 "느리고 GIL에 갇힌" 언어였다. 2024~2025년의 Python은 free-threading(No-GIL) 실험, JIT 컴파일러, uv라는 혁명적 패키지 매니저, Polars의 Rust 백엔드 DataFrame, Pydantic v2의 Rust Core, FastAPI의 타입 안전 API, 그리고 LangChain·LangGraph·Pydantic AI로 이어지는 AI 에이전트 스택까지 — 완전히 다른 언어가 되고 있다. 이 글은 Python 3.13 시대의 Python을 현대적으로 재학습하는 가이드다. Season 2의 다섯 번째.- Published on
AI 하네스(Harness)는 AI 모델을 감싸서 제어하는 오케스트레이션 레이어입니다. System Prompt, Tools, Context, Skills, Hooks, Permissions, Memory의 7가지 구성 요소를 해부하고, Claude Agent SDK와 LangGraph로 직접 만드는 방법까지 — AI 엔지니어 필독 가이드.