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Graph RAG: Improving RAG with Knowledge Graphs — AI Expert OÜ

Graph RAG: Improving RAG with Knowledge Graphs

16 minutesAdvancedAI for BusinessUseful with contextTool UI may have changedReviewed May 18, 2026

Prompt Engineering. A focused walkthrough of Microsoft's GraphRAG — entity extraction, community summaries, query-focused summarization — set up on a local machine with cost notes. Watch it for the graph-RAG section of the article specifically; the cost discussion is the part most write-ups skip.

AI Expert note

Setup commands and costs can drift, but the architecture is useful. Recalculate current token costs and validate whether graph construction is justified before adopting this pattern.

What you should get from this

Understand the Microsoft-style GraphRAG flow: entity extraction, communities, summaries and query-focused synthesis.

Watch or know first

Know why plain vector search can miss relationship-heavy questions.

Practical next step

Watch with the companion article open, then write down one workflow or decision this changes for your own work.

Watch next

Continue through the same learning path with the next curated companion videos.

Further reading

Classic chunk-based RAG has limits. Graph RAG, agentic RAG, and long-context RAG each break those limits in different ways. When each is the right tool, how they actually work, and the production trade-offs that matter.

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