---
title: "Comment by Ali Muwwakkil on \"RAG vs Fine-Tuning: When to Use Each (and When to Combine Them)\" | Hashnode"
description: "In our recent accelerator, we noticed that combining RAG and fine-tuning often yields the best results when deploying AI in production settings. RAG is excellen"
url: "https://hashnode.com/posts/rag-vs-fine-tuning-when-to-use-each/69e4c9fe1953a68e44639b6a/comment/69e4ce6586138a00819a06a0"
---

[Discussion](https://hashnode.com/posts/rag-vs-fine-tuning-when-to-use-each/69e4c9fe1953a68e44639b6a)

Comment [RAG vs Fine-Tuning: When to Use Each (and When to Combine Them)](https://hashnode.com/posts/rag-vs-fine-tuning-when-to-use-each/69e4c9fe1953a68e44639b6a)

[Ali Muwwakkil](https://hashnode.com/@enterpriseaileadersh)

[Apr 19](https://hashnode.com/posts/rag-vs-fine-tuning-when-to-use-each/69e4c9fe1953a68e44639b6a/comment/69e4ce6586138a00819a06a0)

In our recent accelerator, we noticed that combining RAG and fine-tuning often yields the best results when deploying AI in production settings. RAG is excellent for ensuring your model stays updated with real-time data, while fine-tuning enhances specific language patterns and domain expertise. However, the key is in balancing them; too much reliance on either can lead to inefficiencies. For example, an over-tuned model might miss out on valuable, timely data. It's about creating a synergy that aligns with your business needs. - Ali Muwwakkil (ali-muwwakkil on LinkedIn)