There’s a moment if you’ve ever asked a language model to write SQL when you realize it doesn’t actually “know” your data.

It can guess. It can hallucinate. But it doesn’t understand the tables, relationships, or field names that make up the living structure of your business. That’s the missing link.

And that’s where the Knowledge Graph comes in.

The Concept: Marrying the Knowledge Graph with LLM Reasoning

Imagine giving an LLM not just general training, but a map of your data’s logic — built from the metadata itself.

With Neo4j, you can ingest your star schema (or snowflake schema) as a graph:

• Tables become nodes
• Fields become properties
• Foreign keys and joins become relationships
• Field definitions, descriptions, and tags become accessible context

This graph becomes a walkable explanation of your data model. And when paired with a reasoning LLM, you’re not just handing it raw data you’re handing it understanding.

Why It Matters

Most LLMs today operate in a vacuum. You ask for a report, and they guess how your data might look. But when you embed a graph of the schema:

• The LLM can discover the right tables by walking relationships
• It can check constraints, foreign keys, and lineage
• It can understand the meaning behind cryptic field names (like col1, acct_id, etc.)
• It can rewrite, adjust, and optimize SQL from a place of real context

You’re essentially giving your AI analyst a map, a compass, and a language guide so it doesn’t get lost in the woods.

What This Enables

In future posts, I’ll walk through how I implemented this inside a Databricks + Streamlit app, including:

1. How I used Neo4j to store schema relationships and field-level metadata
2. How I passed that graph context to the LLM to generate accurate, dynamic SQL
3. How I closed the loop sending the SQL query to Databricks, returning the result, and letting the LLM explain it in plain English

But this post is about the idea the strategy.

If you want LLMs to reason with your data, they need to know your data.

A knowledge graph is how you teach them.

Teaching AI to Query – Part 2: From Knowledge Graph to Dynamic SQL with Claude, Neo4j, and a Dash of Prompt Engineering