LLM Fine-Tuning: When It's Worth It, Costs & Pitfalls

When should you fine-tune an LLM—and when are prompting or RAG enough?

In this free 42-minute video, learn how fine-tuning works, where it adds value, and which pitfalls can make it expensive or ineffective.

• Understand the LLM training stages, from pre-training to supervised fine-tuning and alignment.

• Choose between fine-tuning, prompting and RAG for your task.

• Compare full model fine-tuning, adapters and LoRA.

• Understand how quantization affects model size and hardware requirements.

• Explore tools such as Hugging Face PEFT, Axolotl and managed fine-tuning APIs.

• Learn why data quality and evaluation matter—and watch out for overfitting and catastrophic forgetting.

For developers and aspiring AI engineers who want to make better decisions before investing in model training.

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