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Fine-Tuning

Further training a pre-trained model on your own data so it masters a specific style, format, or task.

Best when you need a consistent tone or output format that prompting alone can't achieve. For adding fresh knowledge, RAG is usually cheaper and easier to keep current than fine-tuning.

Related resources

Hands-on LLM Fine-Tuning
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.