#GraphRAG

GraphRAG combines knowledge graphs with retrieval-augmented generation for structured, relationship-aware retrieval. Articles cover GraphRAG architecture, BigQuery-based knowledge graph construction, and how graph-structured retrieval improves answer quality for questions involving entity relationships.

2 posts tagged with graphrag. ← All posts

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Pratik Dhanave · ·7 min read

GraphRAG — Grounding an LLM in a Knowledge Graph

This is the post the whole series was building toward. Retrieval-augmented generation gives an LLM relevant text to ground its answers, but plain RAG retrieves disconnected chunks and misses the relationships between facts. GraphRAG grounds the model in a knowledge graph instead — so it can follow relationships, reason across multiple hops, and answer questions that no single chunk contains. It's where ontologies stop being theory and become the thing that makes an AI system trustworthy on your domain.

The post the series built toward. Plain vector RAG retrieves disconnected chunks and misses relationships; GraphRAG retrieves a subgraph by traversing the knowledge graph, so multi-hop answers come from walked edges, not guessed connections — typed, verifiable, citable. When to use it, and why hybrid often wins.

#Knowledge Graphs#GraphRAG#RAG#LLMs#Ontology#Semantic Web

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.