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  • It becomes outdated as facts change.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