· LLM Finetuning · 2 min read
RAG vs. Fine-Tuning: Choosing the Right Strategy for Your Data
Should you retrain the model or give it a textbook? We break down when to use RAG vs. Fine-Tuning (or both) for enterprise AI.
One of the most common questions we get from CTOs is: “Should we fine-tune a model on our documents, or use RAG?” The answer, as usual, is “It depends.” Both methods solve the problem of adding custom knowledge, but they do it in different ways.
Retrieval-Augmented Generation (RAG)
RAG works by looking up relevant information in a database and sending it to the AI along with your question. It’s like giving a student an open-book exam.
- Best For: Facts that change often (like stock prices or news), and situations where you need to check citations.
- Pros: Cheaper to set up, easy to update, and trustworthy (less likely to make things up).
- Cons: Can be limited by how much text fits in the “prompt,” and slightly slower to answer.
Fine-Tuning (FT)
Fine-Tuning is training the model to remember information permanently. It’s like sending the student to medical school for 4 years.
- Best For: Teaching a specific style, tone, or complex behaviour (like writing code in a specific way).
- Pros: Faster answers (shorter prompts), and better at following complex instructions.
- Cons: Expensive to train, hard to update (you have to re-train to add new knowledge), and can sometimes hallucinate facts.
The Hybrid: RAG + Fine-Tuning
For the best enterprise systems, the gold standard is often using RAG + Fine-Tuning together. You Fine-Tune the model to teach it how to think and behave like your best employee. Then, you use RAG to give it the specific facts it needs to answer the question.
Alps Agility takes a pragmatic approach. We usually start with RAG to get value quickly, and then layer in Fine-Tuning later once we have gathered enough data.
Unsure which path to take? Book a workshop to define the AI strategy that fits your reality.
