A general-purpose model scored 85% on our finance benchmark. After domain adaptation, it scored 96%.

The 11% difference? That's the difference between a demo and a production system in a specialized industry.

Domain adaptation is the most reliable way to dramatically improve model performance without collecting massive new datasets.

What works: continued pre-training on domain text. Take your base LLM and further train it on millions of tokens of finance documents (or legal, medical, engineering — whatever your domain). This teaches the model domain vocabulary, conventions, and patterns.

Then fine-tune on task-specific examples. A few hundred labeled examples of your exact task.

The two-step approach (continued pre-training + task fine-tuning) consistently outperforms either step alone in my experience.

For embeddings, same principle: domain-adapted embedding models retrieve more relevant documents in specialized domains than general-purpose ones. The difference is especially noticeable for technical terminology.

The mistake engineers make: skipping continued pre-training and jumping straight to fine-tuning. Fine-tuning teaches the model HOW to respond. Continued pre-training teaches it the domain LANGUAGE. You need both.

If you're deploying AI in a specialized industry, domain adaptation isn't optional. It's the difference between "technically works" and "actually useful."