Should you fine-tune, or is RAG enough?

Everyone argues fine-tuning versus RAG, but which one does my actual, narrow task need?

BeginnerRead 5 minDoing: twenty to thirty minutes, mostly writing and checking, once you know the task6 steps

▶ Start

What you'll have at the end

A one-paragraph decision, written down, saying fine-tune or RAG and the one sentence why

Before you start

You need

One specific task in mind, described precisely enough to say what the model receives and what it must hand back, plus a rough sense of how many times a day it will run.

Not covered

Building the retrieval pipeline or running the fine-tuning job itself; this recipe only produces the written decision.

Leans on

Pin down exactly what the model has to do

Write your task as one exact sentence
Split that sentence into two questions

Score each half against real evidence

Test the knowledge question against real signals
Test the behavior question the same way

Weigh the real cost, then decide

Compare the real operating cost of each
Write the one-paragraph decision down

Where to go next

Format your first fine-tuning dataset

the decision lands on fine-tune and you're ready to shape your own labeled examples into a format a trainer accepts

Ground RAG answers in your own documents

the decision lands on RAG and you're ready to wire a chatbot to your own documents

Checked 15 Aug 2026

Part of the LLM Fine-tuning cookbook