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Devanshu Biswas
Devanshu Biswas

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Fine-tuning vs RAG: Two Ways to Teach an LLM

#rag #ai #llm #beginners

Want an LLM to know your private docs or this week's facts? You have two very different options — RAG and fine-tuning — and people constantly pick the wrong one. The rule is simple: facts → RAG, behaviour → fine-tune.

🧩 Pick a scenario, see which wins: https://dev48v.infy.uk/ai/days/day10-finetune-vs-rag.html

RAG — give it notes at question time 

answer = llm(retrieve(question) + question);  // open-book, weights unchanged
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Your data stays OUTSIDE the model; you fetch the relevant bits and paste them into the prompt. Best for KNOWLEDGE, especially facts that change — update the docs, not the model. Cheap, instant to update, can cite sources.

Fine-tuning — change the weights 

tunedModel = train(baseModel, yourExamples);  // behaviour baked in
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Continue training on examples so a new pattern is internalised. Best for BEHAVIOUR: a consistent tone, format, or narrow skill applied everywhere. Needs a training run + curated examples, and goes stale on facts (retrain to update).

The decision

Ask: is this a fact, or a way of behaving?

  • Changing catalogue / policies / news → RAG
  • Always reply on-brand / always output this JSON → fine-tune

They combine

Fine-tune for HOW it answers + RAG for WHAT facts it uses. A support bot can be fine-tuned to sound on-brand AND RAG over the live help-centre. Most teams reach for prompting + RAG first, and fine-tune only when needed.

Run the scenarios.

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