RAG vs Fine-Tuning vs Orchestration: Picking the Right Lever
Three techniques get pitched as solutions to the same problem. They're not — they solve different parts of the stack. A practical decision tree for engineering teams.
They are not alternatives
RAG, fine-tuning and orchestration get compared as if a team has to pick one. They solve three different problems and most production systems use all three.
What each one actually does
| Technique | Problem it solves | What it costs |
|---|---|---|
| RAG | Model doesn't know your private data | Vector store + retrieval latency |
| Fine-tuning | Model doesn't match your tone, format or domain vocabulary | Training run + per-deployment cost |
| Orchestration | One LLM call can't complete the task | Scheduler, observability, multiple model calls per task |
Decision tree
- Is the model missing facts? → RAG.
- Right facts, wrong style? → Fine-tuning (or a system prompt first).
- One prompt not enough to finish the task? → Orchestration.
- Is the answer "all three"? Usually, yes.
What this looks like together
A production support agent typically (1) fine-tunes a small model on the brand's voice, (2) uses RAG to pull the right KB article, (3) runs both inside an orchestrated graph that also calls a refund tool and a verifier agent. Drop any layer and quality drops.
Where to start
If you have not built anything yet, start with RAG + a strong system prompt on a frontier model. Add orchestration when one agent isn't enough. Add fine-tuning last — highest effort, lowest flexibility, rarely the bottleneck.
Terms in this post: LLMRAGVector DatabaseFine-Tuning