AI Skills & How They Work
Fine-Tuning vs RAG: When to Use Which
Fine-tuning changes model weights to teach new behavior or style; RAG keeps weights frozen and injects relevant documents at query time. They solve different problems, cost different amounts, and fail differently. Most production systems use one or the other, and many use both for different parts of the stack.
Points clés
- RAG adds knowledge without retraining: retrieve relevant documents and include them in the prompt, so facts stay current and auditable.
- Fine-tuning changes behavior: it shifts style, format, and task performance by adjusting weights on curated examples.
- RAG is cheaper to update: changing a document updates answers immediately; fine-tuning needs data collection, training, and redeployment.
- Fine-tuning helps consistency: when you need a specific voice, format, or domain reasoning, weights carry it better than long prompts.
- RAG helps accuracy on facts: it grounds answers in source documents you control, which reduces hallucination on specific data.
- Fine-tuning risks forgetting: training on new data can degrade general capabilities; RAG does not touch the base model.
- RAG risks retrieval failures: wrong or missing documents produce wrong answers even with a perfect model.
- The practical default: start with RAG plus good prompts, add fine-tuning only when evals show a gap prompts cannot close.
Questions fréquentes
Can I use both together?
Yes, and many do: fine-tune for style and format, RAG for current facts and citations.
Which is cheaper?
RAG is usually cheaper to iterate; fine-tuning has upfront cost but lower per-query overhead at scale.
Does fine-tuning reduce hallucination?
On the trained domain somewhat, but it can increase confident wrong answers if the data is flawed.
How much data does fine-tuning need?
Quality beats quantity: hundreds of well-crafted examples often beat thousands of noisy ones.
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