RAG vs. Fine-Tuning: A Decision Framework
The Context Dilemma
When an LLM doesn't know your business data (which it doesn't, by default), you have two main architectural choices to fix it:
- Fine-Tuning: "Teaching" the model new information by updating its neural weights.
- RAG (Retrieval-Augmented Generation): "Showing" the model the information at runtime.
At Svalio, we get asked this question daily. The answer is almost always RAG, but let's break down why.
Retrieval-Augmented Generation (RAG)
RAG is essentially an open-book test. We index your company's PDFs, Notion pages, and SQL databases into a Vector Database. When a user asks a question, we semantic-search the database, retrieve the top 3 relevant chunks, and paste them into the prompt.
Pros:
- Accuracy: The model can cite its sources (e.g., "See Employee Handbook, page 12").
- Freshness: Update the database instantly. No re-training needed.
- Security: You can apply ACLs (Access Control Lists) at the retrieval step.
When to Fine-Tune?
Fine-tuning is expensive and slow. However, it excels at style transfer and domain adaptation.
Do you need the AI to write code in a proprietary internal language that GPT-4 has never seen? Fine-tune it. Do you need it to speak in a specific "brand voice" that is sarcastic and witty? Fine-tune it.
The Hybrid Approach
For our enterprise clients, we often use a hybrid approach: We use RAG used for knowledge and Fine-Tuning for behavior. This gives you the best of both worlds: a model that knows how to act (weights) and knows what is true (context).