How Vector RAG Works
Vector RAG is the most common approach to giving AI systems access to your data:
- Chunking — Documents are split into pieces (typically 500-2000 characters)
- Embedding — Each chunk is converted to a numerical vector (a list of numbers representing its semantic meaning)
- Storage — Vectors are stored in a vector database
- Retrieval — When you ask a question, your question is also vectorized, and the system finds the “closest” chunks by mathematical similarity
- Generation — The retrieved chunks are fed to an AI model along with your question to generate an answer
Where Vector RAG Works
Vector RAG excels at:
- Document search — “Find the section about refund policies”
- FAQ answering — “What’s our return window?”
- Content summarization — “Summarize this contract”
For straightforward retrieval where the answer lives in a single document or chunk, vector RAG is fast, simple, and effective.
Where Vector RAG Breaks Down
Vector RAG struggles with business operations questions because they require multi-hop reasoning — connecting information across multiple sources:
- “Which clients are at risk?” requires connecting engagement data, contract dates, communication patterns, and sentiment signals
- “What should I know before this meeting?” requires traversing relationships between people, projects, past interactions, and recent changes
- “Why did we lose the Anderson deal?” requires understanding a sequence of events across CRM, email, and meeting notes
Vector RAG finds similar text. Business operations require understanding relationships.
The 3.4x Gap
In benchmarks on multi-hop business reasoning tasks, GraphRAG delivers 3.4x higher accuracy than vector RAG (Diffbot Benchmark). The gap exists because:
- Vector RAG retrieves chunks in isolation — it doesn’t understand how they connect
- GraphRAG traverses relationships — it follows the connections between entities
- Business questions are almost always about connections, not isolated facts
Not Either/Or
Vector RAG isn’t wrong — it’s incomplete. Many production systems use both: vector RAG for document-level retrieval and GraphRAG for relationship-aware reasoning. The key is knowing which tool fits which question.