RAG vs Fine-Tuning
When you need to adapt an LLM to your specific data or task, you have two main options: RAG or fine‑tuning. They are not mutually exclusive; sometimes you use both. This chapter helps you choose the right approach.
Fine‑Tuning
Fine‑tuning continues training a pre‑trained model on a smaller, task‑specific dataset. It changes the model's weights, making it better at a particular style or format. Examples: teaching a model to follow instructions (instruction tuning), or to mimic a specific writing voice.
- Changes model behaviour permanently.
- Can improve reasoning and style.
- ❌ Requires expensive GPU time and expertise.
- ❌ Needs a large, high‑quality dataset.
- ❌ Does not automatically incorporate new knowledge; retraining needed.
RAG
RAG leaves the model unchanged. It retrieves relevant information from an external knowledge base at inference time and adds it to the prompt.
- No training cost; works with any LLM (even closed APIs like GPT‑4).
- Always up‑to‑date (just update the knowledge base).
- Can cite sources.
- ❌ May not improve the model's inherent reasoning or style.
- ❌ Retrieval quality is critical; poor retrieval leads to poor answers.
When to Use Which?
- RAG first: For knowledge‑intensive tasks, private documents, up‑to‑date information.
- Fine‑tuning: For teaching the model a specific tone, format, or reasoning pattern (e.g., SQL generation, medical coding).
- Combine them: Fine‑tune for style, then add RAG for knowledge.
Two Minute Drill
- Fine‑tuning changes model weights; RAG does not.
- RAG is best for knowledge and private data.
- Fine‑tuning is best for teaching behaviour and style.
- You can use both together.
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