Should I fine-tune or use RAG?

Use RAG for knowledge, fine-tuning for style/format or narrow tasks — often you combine both.

How much data do I need for a fine-tune?

Less than most people fear: for style or format adaptation with LoRA, hundreds to a few thousand high-quality examples usually move the needle. Quality dominates quantity — and always carve out an evaluation set first, or you cannot tell whether the tune helped.

Can I fine-tune a closed-source model?

You cannot fine-tune closed weights locally. Closed providers offer hosted fine-tuning APIs instead; otherwise stick to prompting or RAG. Full fine-tuning is only an option for open-weight models.

GuideAdvanced

Hands-on LLM Fine-Tuning

Item: Hands-on LLM Fine-Tuning
Rating: 4.7
Author: AI Resource Hub

A practical, hands-on guide to fine-tuning large language models, covering parameter-efficient approaches like LoRA and QLoRA alongside traditional full fine-tuning. It walks through dataset preparation, hyperparameter tuning, and evaluation metrics with real code examples. Aimed at ML engineers who want to adapt base models to specific domains without the cost of training from scratch.

Overview

"Hands-on LLM Fine-Tuning" is a "Guide" resource curated by AI Resource Hub, filed under the Tutorials category and suited to Advanced-level learners. It is provided by AI Resource Hub, was last updated on 2026-06-28, and holds an editorial score of 4.7/5 from our team. Click "Visit Resource" on the right to open the original page.