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.
Model names, pricing and capabilities change quickly. Use this for the decision pattern, then verify current model behavior before adopting it.
Place fine-tuning inside the broader training pipeline and decide when it is better than prompting or RAG.
General ML literacy; it's a conceptual walkthrough, so no code-along setup is needed.
Last reviewed: May 18, 2026
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