Developing an LLM: Building, Training, Finetuning
Sebastian Raschka. Sebastian Raschka's slower walkthrough of where fine-tuning sits in the broader LLM training pipeline — instruction tuning, classification fine-tuning, parameter-efficient methods, and the trade-offs the article calls out before recommending LoRA. Good calibration before you start, especially if your team is debating whether fine-tuning is even the right step.
AI Expert note
Model names, pricing and capabilities change quickly. Use this for the decision pattern, then verify current model behavior before adopting it.
What you should get from this
Place fine-tuning inside the broader training pipeline and decide when it is better than prompting or RAG.
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