6-11: Graph RAG Implementation Economics
Why standard vector search fails at complex reasoning and how Knowledge Graphs (Neo4j) solve multi-hop query hallucination.
🎯 What You'll Learn
- ✓ Compare Vector similarity vs Graph traversal
- ✓ Calculate Ontology construction ROI
- ✓ Minimize complex query hallucinations
The Failure of Cosine Similarity
Standard RAG uses vector embeddings. It is great at answering "find documents that sound like this." It fails catastrophically at multi-hop reasoning like "Who is the manager of the person who approved this PR in 2022?"
Graph RAG solves this by pre-computing relationships (nodes and edges). Instead of guessing via text similarity, the LLM writes an explicit Cypher query to definitively traverse the graph.
However, Graph RAG is substantially more expensive. Designing an ontology (the schema) and maintaining an enterprise graph database (Neo4j) requires specialized talent and carries high CapEx vs off-the-shelf vector stores.
The drop in standard RAG accuracy when a user asks a question requiring > 2 distinct facts to be joined.
The cost of continuously extracting nodes/edges from unstructured text via an LLM pipeline.
Execute a pilot integration of a dual retrieval system.
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Module Syllabus
Lesson 1: The Failure of Cosine Similarity
Standard RAG uses vector embeddings. It is great at answering "find documents that sound like this." It fails catastrophically at multi-hop reasoning like "Who is the manager of the person who approved this PR in 2022?"Graph RAG solves this by pre-computing relationships (nodes and edges). Instead of guessing via text similarity, the LLM writes an explicit Cypher query to definitively traverse the graph.However, Graph RAG is substantially more expensive. Designing an ontology (the schema) and maintaining an enterprise graph database (Neo4j) requires specialized talent and carries high CapEx vs off-the-shelf vector stores.
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Richard Ewing — AI Economist & Capital Auditor