Two Different Problems, Two Different Solutions
RAG and fine-tuning solve different problems. RAG grounds model outputs in your specific, frequently-changing data. Fine-tuning changes the model’s behaviour, tone or task-specific reasoning patterns.
When RAG Is the Right Choice
RAG is ideal when your knowledge base changes frequently (product catalogs, policy documents, support tickets), when you need source citations for trust and auditability, and when you want to avoid the cost and complexity of retraining models.
When Fine-Tuning Is the Right Choice
Fine-tuning is better suited for teaching a model a consistent tone/style, a specialized task format (e.g. structured extraction), or domain-specific reasoning patterns that go beyond what can be captured in a prompt or retrieved context.
The Hybrid Approach We Recommend
For most enterprise use cases, we recommend starting with a well-engineered RAG pipeline, and layering in light fine-tuning only where prompt engineering and retrieval alone cannot achieve the required consistency or format compliance.
Get an AI Architecture Assessment
Our AI Development team can assess your use case and recommend the right blend of RAG, fine-tuning and prompt engineering for your specific business problem.