ARCHITECTURE GUIDE

RAG vs Fine-Tuning

A simple framework to choose the right approach for your data, timeline, and business goals.

When RAG is best

  • Your knowledge changes frequently
  • You need source traceability and citations
  • Fast iteration matters more than model retraining
  • You need secure access by user role

When fine-tuning is best

  • You need specific tone/format behaviors
  • Task behavior is stable and repeatable
  • Low latency pattern responses are critical
  • You can maintain model update pipelines

Hybrid pattern (common)

  • Fine-tune for behavior style + task bias
  • Use RAG for fresh/private knowledge
  • Add evaluation and safety guardrails
  • Instrument for cost and quality monitoring
Related resources
RAG Development Services AI Agent Development Services AI Agent Cost Guide RAG Implementation Checklist
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FAQ

RAG vs Fine-Tuning FAQ

Which should we start with?

If knowledge changes frequently, start with RAG. If behavior consistency is key, evaluate fine-tuning.

Can both be used together?

Yes. Hybrid architectures combine fine-tuned behavior with retrieval over private and frequently updated knowledge.

What is faster to ship?

RAG is often faster for enterprise use because retrieval updates are easier than model retraining cycles.

How do we decide with confidence?

Run a scoped pilot with evaluation metrics on accuracy, latency, and cost before scaling.