Fine-tuning isn't dead, it's just not the default anymore
In-context learning won the spotlight from today's decoder LLMs, but fine-tuning still wins on consistency, control and deep domain knowledge.
Fine-tuning isn’t dead. It’s just not the default anymore.
Back in the BERT days, fine-tuning was the way to adapt a language model for a specific task. But with today’s powerful decoder-based LLMs, in-context learning approaches (like direct prompting, RAG, and whatever else you can think of) have taken the spotlight, and for good reason: they work really well.
That said, fine-tuning still has its place. Yes, you can often get by with prompts, but when you need consistency, control, or deep domain knowledge, fine-tuning might still be the better choice.
Here are a few cases where fine-tuning shines:
- Custom chatbots – Want your bot to speak in your brand’s voice or tone? Fine-tuning helps lock that in.
- Text classifiers – Prompts can guide outputs, but fine-tuning (especially on a strong base model) gives better reliability.
- Domain-specific translation – Legal, medical, or technical jargon? Prompting helps, but fine-tuning really understands the lingo.
- Low-resource languages – Fine-tuning can boost performance when there’s not much data or support for the language.
So no, fine-tuning isn’t dead. It’s just hanging out in the toolbox, waiting for the right job.