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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.

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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Outdated or policy-inaccurate answers when information changes.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.

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