Knowledge Graphs as Cognitive Middle Layer

Beyond their use as retrieval backbones in RAG systems, knowledge graphs are increasingly positioned as a cognitive middle layer between raw input and LLM reasoning.

In this conception, the KG provides structured scaffolding for querying, planning, and decision-making. It’s not just about retrieving relevant context, it’s about enabling more interpretable and grounded generation.

The distinction matters: RAG treats the knowledge base as a lookup table (find relevant chunks, feed them to the model). The cognitive middle layer treats it as reasoning infrastructure (traverse connections, validate inferences, ground decisions in explicit relationships).

This positions knowledge graphs as enabling explainable AI in ways that vector retrieval alone cannot. When reasoning proceeds through explicit graph traversal, the path becomes auditable.

Related: 06-atom—llm-kg-paradigm-inversion, 07-molecule—vectors-vs-graphs