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RAG and GraphRAG

RAG (Retrieval-Augmented Generation) helps models answer using your data, not only their pretraining knowledge.

RAG in plain terms​

Basic flow:

  1. User asks a question.
  2. System retrieves relevant documents/chunks.
  3. Model answers using that retrieved context.

This improves factual grounding and keeps answers tied to your content.

When to use RAG​

  • Internal knowledge assistants
  • Support bots over documentation
  • Domain Q&A where freshness and citations matter

Common RAG failure modes​

  • Poor chunking strategy
  • Weak retrieval quality
  • No citation or source visibility
  • No evaluation on real user questions

What GraphRAG adds​

GraphRAG combines retrieval with graph structures (entities/relationships), which can help with:

  • Multi-hop reasoning across connected facts
  • Better handling of structured domain relationships
  • Improved context selection in complex knowledge spaces

When GraphRAG is worth it​

  • Your domain has rich relationships (people, systems, dependencies, events)
  • Standard vector RAG is missing relevant connected context

When GraphRAG is not worth it​

  • Your data is simple and mostly document-like
  • You do not have enough usage to justify extra system complexity

Practical advice​

Start with well-implemented standard RAG. Move to GraphRAG only after you can show concrete retrieval gaps that graph structure solves.