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Retrieval-Augmented Generation

Retrieval-augmented generation adds a search step before a model writes, so it can answer from documents it was never trained on and point to its sources. The pattern covers a wide range of systems, from a simple vector lookup to graph-aware retrieval. The guide here compares those data structures and shows which questions each one can actually answer.

1 guide on Retrieval-Augmented Generation.

Reference Guide · 16 min read

Graph Types for AI Agents

The six structures behind modern AI search, taxonomy, ontology, knowledge graph, information graph, context graph, vector / embedding index, what each one actually is, when to use which, and how they combine into GraphRAG. One dataset, six structures, visualized in 2D and 3D.

Knowledge Graphs Vector Databases Ontology GraphRAG RAG Retrieval-Augmented Generation Context Graph Semantic Search AEO GEO AI Agents Schema.org

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A Helping Hand for LLMs (Retrieval Augmented Generation) - Computerphile video · Computerphile
A Practitioner's Guide to Retrieval Augmented Generation guide · Cameron R. Wolfe
Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG (arXiv) paper · arXiv
Retrieval-augmented generation (Wikipedia) guide · Wikipedia
Retrieval-Augmented Generation paper · Meta (2020) paper · arXiv
What is RAG (Retrieval Augmented Generation)? | IBM guide · IBM
What is RAG? - Retrieval-Augmented Generation AI Explained - AWS guide · AWS
What is retrieval-augmented generation? post · Alex Xu

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AEO AI Agents Context Graph GEO GraphRAG Knowledge Graphs Ontology RAG

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Retrieval-Augmented Generation

Graph Types for AI Agents

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