Add a Knowledge Graph 3x better

If your AI agent doesn’t know when it’s wrong, it doesn’t belong in production. We reviewed a recent study that tested LLM pipelines against enterprise data environments, benchmarking their performance on enterprise datasets using the Yale Spider schema. Same model. Same questions. Different architecture. Here’s what changed: SQL-only → 17.28% accuracy Add a Knowledge Graph → 3x better Add ontology-based query checks + repair loop → 72.55% accuracy That's not incremental progress. That’s a systems-level shift. Here’s what mattered most: 70% of fixes came from domain constraints in the query body Most gains showed up in complex schema environments—think KPIs and strategic planning And when the model couldn’t repair itself? It admitted it with “unknown,” cutting hallucinated outputs by a huge margin The architecture looks like this: Ontologies validate logic pre-execution Knowledge graphs serve as real-time reasoning layers LLM Repair loops handle failure cases autonomously FalkorDB is already solving the low-latency challenge here—serving graphs in real time for reasoning-heavy queries. The lesson: You don’t need smarter prompts. You need systems that can detect when the logic breaks—and fix it before it hits the user.

Apr 9, 2025 - 08:40

If your AI agent doesn’t know when it’s wrong, it doesn’t belong in production.

We reviewed a recent study that tested LLM pipelines against enterprise data environments, benchmarking their performance on enterprise datasets using the Yale Spider schema.

Same model. Same questions. Different architecture.

**Here’s what changed:

**SQL-only → 17.28% accuracy
Add a Knowledge Graph → 3x better
Add ontology-based query checks + repair loop → 72.55% accuracy

That's not incremental progress. That’s a systems-level shift.

Here’s what mattered most:

70% of fixes came from domain constraints in the query body
Most gains showed up in complex schema environments—think KPIs and strategic planning
And when the model couldn’t repair itself? It admitted it with “unknown,” cutting hallucinated outputs by a huge margin

The architecture looks like this:

Ontologies validate logic pre-execution
Knowledge graphs serve as real-time reasoning layers
LLM Repair loops handle failure cases autonomously
FalkorDB is already solving the low-latency challenge here—serving graphs in real time for reasoning-heavy queries.
The lesson: You don’t need smarter prompts. You need systems that can detect when the logic breaks—and fix it before it hits the user.