Why use RAG instead of fine-tuning?

RAG keeps the knowledge base separate from the model, so you can update content without retraining. Fine-tuning bakes knowledge into model weights, which is slower and harder to update. RAG is preferred when your data changes frequently.

RAG (Retrieval-Augmented Generation)

Written by Max Zeshut

Founder at Agentmelt · Last updated Sep 9, 2026

A technique that combines retrieval (fetching relevant documents from a knowledge base or database) with LLM generation. The model uses retrieved context to produce accurate, cited answers. RAG powers support agents that answer from your KB and legal agents that cite precedents.

Example

A support agent receives a customer question, searches the help center for relevant articles, and generates an answer that cites the specific article—reducing hallucination and building trust.

Frequently asked questions

Why use RAG instead of fine-tuning?: RAG keeps the knowledge base separate from the model, so you can update content without retraining. Fine-tuning bakes knowledge into model weights, which is slower and harder to update. RAG is preferred when your data changes frequently.

See it as a workflow

Support Ticket Deflection Workflow

Trigger, steps, n8n nodes, guardrails and an importable template — plus what it costs to have it built.

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