---
id: "1950182433342841203"
requested_id: "1950182433342841203"
status: ok
level: 1
user: "akshay_pachaar"
name: "Akshay 🚀"
created_at: "2025-07-29T13:11:39.000Z"
in_reply_to: "1950182409011671363"
quoted: null
source: tweet-result
url: "https://x.com/akshay_pachaar/status/1950182433342841203"
via:
  - quote: "1950496511550169518"
media:
  - data/external_media/1950182433342841203-0.jpg
---

Graph RAG solves this by:

- Building a graph with entities and relationships from docs.
- Traversing the graph for context retrieval.
- Sending the entire context to the LLM for a response.

The visual shows how its different from naive RAG: https://t.co/JFYkot6xIv