#Agentic RAG
8 related articles
Agentic RAG Design Guide: When to Retrieve, Which Data to Select, and Full Traceability Explained
A deep dive into Agentic RAG system design: how AI agents decide when to retrieve, select multi-source data, and trace answer sources. Based on Reddit community discussions, offering actionable solutions for routing, multi-path recall, and citation verification.
Footnote: Sentence-Level Citation Agentic RAG That Makes Every Answer Verifiable
Footnote is a sentence-level citation Agentic RAG research agent: it plans multiple retrievals, citations are API-validated rather than model-typed, and it says 'I don't know' when nothing is found. This article analyzes its architecture, BM25+vector hybrid retrieval fusion strategy, and why 'verifiable citations' are the real hard part of RAG.
Skill Extraction Under the SFIA Framework: Is Agentic RAG Actually Better?
New arXiv paper formalizes SFIA-based skill extraction, comparing five strategies from lexical baselines to multi-agent RAG. Key findings: retrieval recalls more, generation is more precise, responsibility levels need explicit modeling, and multi-agent doubled latency with no accuracy gain.
TutorialsComplete Guide to Agentic RAG: Principles, Architecture, and Hands-On Implementation
Deep dive into Agentic RAG principles and implementation. Compare traditional RAG limitations, learn tool-based retrieval with ReAct Agent loops, with LangChain/LangGraph code examples.
TutorialsAgentic RAG Practical Guide: The Complete Evolution from Traditional RAG to Agent-Based Retrieval
Deep dive into Agentic RAG vs traditional RAG, with ChatPDF case study and LangChain code walkthrough covering tool design, multi-turn iteration, and autonomous decision-making for LLM engineers.
Deep DivesThe Complete Guide to Agentic RAG: Principles and Code Implementation
Deep dive into Agentic RAG vs traditional RAG, covering tool calling, multi-step iteration, query rewriting, with LangChain and LangGraph code examples for building intelligent retrieval systems.
TutorialsAgentic RAG in Practice: Architecture Deep Dive and LangChain Implementation Guide
Compare traditional RAG vs Agentic RAG architectures, explore planning, tool use, and multi-step iteration capabilities, with full LangChain/LangGraph ReAct Agent code and ChatBoss project examples.
TutorialsAgentic RAG in Practice: Architecture Deep Dive and LangChain Implementation
Deep dive into Agentic RAG vs traditional RAG, covering planning, tool calling, and multi-step iteration capabilities with complete LangChain and LangGraph code implementation.