And why your next AI project might, too
If you’ve been following AI developments lately, you’ve probably noticed that Large Language Models (LLMs) — the tech behind tools like ChatGPT — are everywhere. They write code, summarize articles, generate marketing copy, and even answer your random 2 a.m. questions about 17th-century trade routes.
But here’s something that doesn’t get talked about enough: LLMs absolutely love graph databases.
Let’s unpack why.
The connection between LLMs and graphs
LLMs are great at understanding context and relationships in language. They figure out that “Alice” and “Bob” are people, that “Bob works at Acme Corp,” and that “Acme Corp” makes “solar panels.”
Now imagine storing that kind of interconnected information in a traditional relational database. You’d have tables for people, companies, products, and maybe dozens of join tables linking them. Every time you ask a complex question — “Who works with Alice on solar panel projects?” — your SQL query turns into a spaghetti mess of joins.
A graph database like Neo4j works differently. Instead of rows and foreign keys, it stores data as nodes (things) and relationships (how they connect). That means queries like:
MATCH (alice:Person {name: 'Alice'})-[:WORKS_WITH]->(colleague)-[:ON_PROJECT]->(p:Project {type: 'Solar Panel'})
RETURN colleague.name;
…are not only easier to read but also way faster for highly connected data.
For LLMs, that’s a perfect match: the data structure aligns with how they already “think” about relationships.
Why LLMs perform better with graph data
- Context is baked in — Graphs store relationships explicitly. LLMs don’t have to reconstruct context from multiple disconnected sources.
- Multi-hop queries are natural — “Friends of friends who live in Paris” is a nightmare in SQL, trivial in Cypher.
- Semantic search integration — You can combine vector embeddings with graph queries to get both meaning and relationships.
- Better grounding for AI — When an LLM pulls from a graph, it can give more accurate, explainable answers because the data connections are clear.
A small example
Let’s say you’re building an AI assistant for a research team. It needs to answer:
“Which authors have co-written a paper with Dr. Lee in the last 5 years?”
In SQL, this means joining authors, papers, authorships, and maybe filtering dates. In Neo4j, it’s more like:
MATCH (lee:Author {name: 'Dr. Lee'})-[:CO_AUTHOR_OF]->(a:Author)
WHERE a.joined_on >= date('2020-01-01')
RETURN a.name;
Readable. Logical. Quick.
Now feed that graph data into your LLM’s context window, and you’ve got a chatbot that not only finds the answer but can also explain the network of collaborations.
Where this is heading
As AI applications get more complex, we’re moving toward knowledge graphs + LLMs as a standard pattern.
- Graph handles the structured, interconnected facts.
- LLM handles natural language understanding and generation.
The result? AI that isn’t just fluent, but informed.
Want to learn Cypher if you already know SQL?
If you’ve worked with relational databases and want to explore Neo4j, I’ve written a practical, side-by-side guide that compares SQL and Cypher queries. It’s written for developers who think in tables but want to start thinking in nodes and relationships.
📚 Relational to Graph: Rethink Data with Graph Databases — perfect if you’re curious about graph databases and want real-world examples without the fluff.
If you’re already experimenting with LLMs, try pairing them with a graph database. You might find that your AI feels a lot less like a parrot and a lot more like a colleague who gets it.
Top comments (1)
Insightful ! 🏆