𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝘃𝘀 𝗥𝗔𝗚: 𝗧𝘄𝗼 𝗪𝗮𝘆𝘀 𝘁𝗼 𝗧𝗲𝗮𝗰𝗵 𝗮𝗻 𝗟𝗟𝗠
You want an LLM to know your private documents or recent news. You have two choices: RAG or fine-tuning. People often pick the wrong one.
The rule is simple. Use RAG for facts. Use fine-tuning for behavior.
RAG (Retrieval-Augmented Generation) This acts like an open-book exam. You keep data outside the model. You fetch relevant information and paste it into the prompt.
- Use it for knowledge.
- Use it for facts that change often.
- It is cheap.
- It updates instantly.
- It cites sources.
Fine-tuning This acts like internalizing a new habit. You train the model on specific examples.
- Use it for behavior.
- Use it for tone and format.
- Use it for narrow skills.
- It requires a training run.
- It requires curated examples.
- It becomes outdated as facts change.
How to choose: Ask yourself if you need a fact or a way of behaving.
Use RAG if you need to update:
- Product catalogs.
- Company policies.
- Daily news.
Use fine-tuning if you need:
- A specific brand voice.
- A strict JSON output format.
The best approach often combines both. Fine-tune for how the model answers. Use RAG for what facts it uses. A support bot uses fine-tuning to sound professional and RAG to access the latest help articles.
Start with prompting and RAG. Move to fine-tuning only when you must.
Test these scenarios here: https://dev48v.infy.uk/ai/days/day10-finetune-vs-rag.html
Source: https://dev.to/dev48v/fine-tuning-vs-rag-two-ways-to-teach-an-llm-3d04
Optional learning community: https://t.me/GyaanSetuAi