---
title: "When Is RAG a Better Choice Than Fine-Tuning for an AI Assistant?"  
description: "When Is RAG a Better Choice Than Fine-Tuning for an AI Assistant?"  
author: "Magdalena Michalska"  
published: 2026-10-15  
canonical: https://answers.mindstick.com/qa/117657/when-is-rag-a-better-choice-than-fine-tuning-for-an-ai-assistant  
category: "Mix Category"  
tags: ["artificial intelligence", "RAG", "Machine learning"]  
reading_time: 1 minute  

---

# When Is RAG a Better Choice Than Fine-Tuning for an AI Assistant?

We’re planning an internal assistant that needs to answer questions about product specifications and company policies. Those documents change every few weeks, so I’m weighing retrieval-augmented generation against fine-tuning a model.

For information that needs to stay current and be traceable to source documents, when is **[RAG versus fine-tuning](https://answers.mindstick.com/qa/117234/when-should-an-enterprise-choose-retrieval-augmented-generation-rag-over-llm-fine-tuning)** the more sensible choice? I’d also like to know what trade-offs teams tend to underestimate, such as retrieval quality, update effort, or the model’s tendency to answer beyond the material it finds.


---

Original Source: https://answers.mindstick.com/qa/117657/when-is-rag-a-better-choice-than-fine-tuning-for-an-ai-assistant

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