LiveUpdated 2026-09-23 10:51 UTC

They all improve how AI uses enterprise knowledge, but they solve very different problems.

Name: They all improve how AI uses enterprise knowledge, but they solve very different problems.
Uploaded: 2026-09-23T09:30:01.000Z
Channel: Aiswarya Venkitesh
Description: Vector RAG retrieves. Agentic RAG reasons. Graph RAG connects. They all improve how AI uses enterprise knowledge, but they solve very different problems. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗥𝗔𝗚 is the right starting point when users need answers from documents, PDFs, policies, and internal knowledge bases. The Azure…

This is a dev post classified by Jev as Databases (a tutorial), kept by the Dev Radar because it carries real work, not commentary.

Vector RAG retrieves. Agentic RAG reasons. Graph RAG connects. They all improve how AI uses enterprise knowledge, but they solve very different problems. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗥𝗔𝗚 is the right starting point when users need answers from documents, PDFs, policies, and internal knowledge bases. The Azure flow typically includes: Data ingestion Content extraction Chunking and tagging Embedding creation Vector indexing Semantic retrieval Grounded answer generation It works best when relevance depends on semantic similarity between a question and stored content. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 goes beyond retrieval. The agent

Posted by Aiswarya Venkitesh (2.1k followers) 1 h ago · 22 likes · 459 views · view the original post on X. Kept by the Dev Radar as Databases.

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