Use RAG when knowledge changes
Policies, product data, tickets, contracts and internal documentation change continuously. RAG can retrieve current evidence without retraining the model.
Freshness, permissions and citation requirements are strong signals that retrieval should be part of the architecture.
Use fine-tuning when behavior is the target
Fine-tuning can improve style consistency, task format, classification behavior or repeated domain-specific patterns. It is less suitable as a replacement for a changing knowledge base.
Training data quality and evaluation coverage determine whether the behavior change is reliable.
Private data creates different controls
RAG can enforce document-level permissions during retrieval. Fine-tuning may embed patterns from training examples into the model, which creates a different governance question.
For sensitive enterprise knowledge, retrieval with explicit ACLs is often easier to reason about operationally.
Cost depends on workload shape
RAG adds retrieval infrastructure and per-request context. Fine-tuning adds dataset preparation, training and model lifecycle work but can sometimes reduce prompt size.
Compare total system cost across expected traffic, latency and update frequency rather than only model pricing.
Many production systems combine both
A fine-tuned model can handle a stable task pattern while RAG supplies current business evidence.
The key is to evaluate each layer independently so teams know whether a failure comes from behavior or missing evidence.
Use an experiment before committing to either path
Build a small evaluation set that represents the target business tasks. Measure whether failures come from missing evidence, poor retrieval, inconsistent output format or a model behavior problem. That diagnosis usually tells you whether retrieval, fine-tuning, prompt changes or application logic should be the next investment.
Do not use architecture labels as goals. The goal is a measurable task outcome with acceptable cost, latency, security and maintainability. RAG and fine-tuning are implementation choices inside that larger product requirement.