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ai deep-dive

How Agents Decide Whether to Retrieve, What to Retrieve, and How to Merge: Three Decision Layers of Agentic RAG

Traditional RAG is a fixed pipeline of 'retrieve then answer.' Agentic RAG splits retrieval into three decision layers: when to retrieve (FLARE uses token probabilities; Adaptive-RAG uses a complexity classifier), what to retrieve (HyDE / RAG-Fusion / decomposition / Step-back), and how to fuse (RRF k=60 then cross-encoder rerank then compression -- Anthropic measured a -67% failure rate reduction). Key counter-intuitive insight: unnecessary retrieval hurts quality -- 'deciding not to retrieve' is a first-class capability.

#rag#agentic-rag#retrieval#ai-agent#llm
ai deep-dive

Auto-Embedding on File Upload Is a Bad Default: A Survey of Adaptive / Agentic RAG and Agentic Parsing

Making 'chunk and embed every uploaded file automatically' the default behavior means making a decision for the LLM that it could have made itself. From Self-RAG (2310.11511) and Adaptive-RAG (2403.14403) to AgenticOCR (2602.24134), the academic trajectory is pushing three layers of decision-making -- whether to retrieve, whether to parse, and how to chunk -- from the ingestion pipeline back to the agent at conversation time.

#rag#agentic-rag#adaptive-rag#tool-use#llm-agent#agentic-parsing#document-parsing
ai guide

Agentic RAG: Letting the LLM Decide When to Search Again

For complex multi-hop questions, a single RAG search isn't enough. Agentic RAG lets the LLM evaluate whether retrieved results are sufficient — if not, it rewrites the query and searches again, forming a ReAct loop.

#rag#agentic-rag#react#multi-hop#llm-agent