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Technical
RAG vs. Fine-Tuning: Which is Right for Your Enterprise?
A technical breakdown of when to inject knowledge (RAG) vs. when to train behavior (Fine-Tuning).
One of the most common questions we get is: "Should we fine-tune Llama 3 on our data?" The answer is usually No. You probably want RAG.
The Mental Model
Think of an LLM as a college student.
- Pre-training: Their entire K-12 education.
- Fine-Tuning: Sending them to med school to learn specific terminology and style.
- RAG: Giving them an open textbook during the exam.
When to Use RAG
- Your data changes frequently (stock prices, inventory).
- You need citations (legal, medical).
- You want to avoid hallucinations.
When to Fine-Tune
- You need the model to speak in a specific "voice" or brand tone.
- You need it to follow a complex, non-standard output format (JSON, SQL).
- You have a huge dataset of "good" examples.
The Sweet Spot: Fine-tune a small model to be good at using tools, then use RAG to give it the knowledge it needs.