The RAG Obituary: Killed by agents, buried by context windows
15 hours ago
- #AI
- #AgenticSearch
- #RAG
- The article discusses the decline of Retrieval-Augmented Generation (RAG) systems due to advancements in context windows and agent-based architectures.
- RAG was initially developed to handle large knowledge bases that exceeded the token limits of early LLMs like GPT-3.5 and GPT-4.
- Chunking documents for RAG presents challenges, such as fragmenting critical information and disrupting document structure.
- Embedding and retrieval pipelines in RAG systems often struggle with semantic search, especially with numerical data and specific terminologies.
- Hybrid search, combining semantic and keyword search, improves retrieval but adds complexity and latency.
- Reranking in RAG systems introduces additional costs, latency, and infrastructure burdens.
- RAG systems have fundamental limitations, including context fragmentation, semantic search failures, and lack of causal understanding.
- The emergence of agentic search, exemplified by Claude Code, offers a new paradigm that eliminates the need for RAG by leveraging large context windows and intelligent navigation.
- Agentic search uses tools like Grep and Glob for direct filesystem access, enabling real-time, precise, and context-rich document analysis.
- The future of AI search lies in agentic systems that can navigate, reason, and understand relationships across entire documents without retrieval fragmentation.