Is your RAG agent hallucinating or missing obvious answers? 😵💫 Basic RAG is just the beginning. We're breaking down 11 advanced strategies to fix its flaws and build a production-ready system.
We’ll talk about:
- A deep dive into 11 distinct RAG strategies, from simple wins like Reranking to advanced techniques like Knowledge Graphs.
- The "Must-Haves": why Context-Aware Chunking and Reranking should be in almost every production RAG system.
- How to use Query Expansion to "cast a wider net" and catch relevant documents even when users ask vague questions.
- The power of Agentic RAG for complex, multi-step questions that require planning and multiple searches.
- Plus, a "RAG Traps" guide to avoid common mistakes like over-engineering and ignoring latency.
Keywords: RAG, Retrieval-Augmented Generation, AI Agents, Vector Database, Knowledge Graph, Reranking, Query Expansion, Contextual Retrieval, AI Engineering, LLM
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정보
- 프로그램
- 발행일2025년 11월 10일 오후 1:41 UTC
- 길이22분
- 등급전체 연령 사용가
