Short answer
Use a knowledge graph when the answer depends on how things connect (customers, products, contracts, regulations). Use vector RAG when you need fast answers from a large and changing pile of documents. Many SMEs should start with vector RAG and add a graph only when relationship questions keep coming up.
Introduction
Two approaches dominate how companies connect their own data to large language models (LLMs): the knowledge graph and vector Retrieval-Augmented Generation (RAG). For Dutch SMEs, choosing the right one can save months of work and a lot of budget.
What is an LLM knowledge graph?
A knowledge graph is a structured way to represent data: entities (a customer, a product, a supplier) and the relationships between them. It makes context explicit, which helps with complex decisions where the links between facts matter as much as the facts themselves.
When to use a knowledge graph
- Complex information needs: Ideal when you work with highly connected data and need those connections to be exact.
- Strategic insight: When decisions depend on seeing relationships, such as who is linked to which contract or risk.
- Reference material: Suited to sectors where detailed reference knowledge is essential, such as healthcare or logistics.
What is vector RAG?
Vector RAG combines retrieval with generative AI. Your documents are split into pieces and turned into vectors (numeric representations of meaning). When someone asks a question, the system finds the most similar pieces and hands them to the model, which writes the answer.
When to use vector RAG
- Speed and efficiency: Right when quick access to specific information is what counts.
- Large datasets: Works well for collections that keep growing and changing, because new documents can simply be added.
- A variety of sources: Ideal when information from different sources has to be combined without a fixed structure.
Knowledge graph vs vector RAG at a glance
- Best at: graph, relationships and multi-step reasoning; vector RAG, finding relevant passages fast.
- Setup effort: graph, higher (you model the data); vector RAG, lower (you index documents).
- Handling change: graph, needs maintenance; vector RAG, re-index new or updated files.
Conclusion
The choice depends on your organisation's needs and environment. Need depth and context? Consider a knowledge graph. Need speed and flexibility? Vector RAG is usually the better start.
At AI Advies Bureau we build tailored strategies for your AI needs. Not sure where to start? An AI readiness assessment shows what fits your business.