# Graph RAG vs Fine-Tuning

Compare Graph RAG and Fine-Tuning across query patterns, AI retrieval quality, and operational fit.

## Quick Verdict

Graph RAG and Fine-Tuning can both be valuable. Teams focused on relationship-native reasoning and explainable context usually prefer graph-centric designs for high-stakes decisions.

| Capability          | Graph RAG                                      | Fine-Tuning                                |
| ------------------- | ---------------------------------------------- | ------------------------------------------ |
| Relationship reasoning | Strong                                         | Varies by model and tooling                 |
| Multi-hop traversal | Native                                         | Often indirect                             |
| Explainability      | Path-level context                             | Depends on pipeline design                 |
| Best fit            | High-context graph analytics, AI grounding, and relationship-first queries. | Use cases that prioritize existing ecosystem alignment and incremental adoption. |

### Graph RAG Strengths

- Strong relationship modeling and traversal depth.
- Useful for multi-hop reasoning and explainable evidence.
- Designed for connected intelligence workflows.

### Fine-Tuning Strengths

- Can fit teams already invested in its ecosystem.
- Works well for workloads aligned to its architecture.
- Can be complementary in a hybrid stack.