‍

RAG vs Fine-Tuning: What’s Better for Customer Support AI?

For customer support AI, RAG (Retrieval-Augmented Generation) is usually the best default because it answers using your latest knowledge sources (KB, solved cases, product docs) without retraining the model.

Fine-tuning can help with consistent style, format, and classification tasks—but it’s not a replacement for up-to-date knowledge.

When to use each:

  • Use RAG when: answers must reflect current docs, policies, and product behavior
  • Use fine-tuning when: you need consistent tone/format or high-quality categorization
  • Use both when: RAG for factual content + fine-tuned components for routing, QA, or structured outputs

How Worknet helps: Worknet is designed to operationalize grounded support workflows using connected sources and can incorporate structured playbooks and controlled behaviors without relying on “model memory” as the source of truth.

‍

This is some text inside of a div block.

What is RAG in customer support?
It’s using retrieval from your knowledge sources to ground the AI’s answers.

Is fine-tuning better than RAG for support answers?
Not usually—fine-tuning doesn’t automatically keep answers up to date when your docs change.

Can you combine RAG and fine-tuning?
Yes—RAG for facts, fine-tuning for style, routing, or classification.

What’s the biggest risk of relying only on fine-tuning?
Outdated or policy-inaccurate answers when information changes.

How does Worknet use RAG effectively?
By grounding responses in connected sources and supporting escalation when the data isn’t sufficient.

‍