Domain adaptation is the process of taking a model trained on broad, general data and making it perform well in a specific domain with its own vocabulary, conventions, and edge cases. A general model knows English; an adapted model knows that in your industry a common word carries a precise technical meaning, that certain abbreviations are unambiguous to insiders, and that documents follow particular structures. The gap between general competence and domain fluency is where many AI projects quietly underdeliver.
There is a spectrum of methods, ordered roughly by cost. Prompt-level adaptation supplies domain context, terminology, and examples in the prompt. Retrieval-based adaptation grounds the model in domain documents at inference time. Fine-tuning, whether full or parameter-efficient with LoRA adapters, actually shifts the model's weights toward domain language and behavior, and continued pretraining on large domain corpora goes deeper still. Embedding models often need adaptation too, since retrieval fails when a general-purpose embedding cannot tell apart two domain terms that look similar but mean different things. The right choice depends on how far the domain sits from general text and how much quality headroom the cheaper methods leave.
At arosplatforms we climb that ladder deliberately in client work. We start with prompting and RAG because they are fast, reversible, and usually sufficient, then measure the remaining error against a domain-specific evaluation set. Only when that evidence shows a persistent gap do we invest in fine-tuning the generation or embedding model, and we test the adapted model for catastrophic forgetting so domain gains never silently cost general capability.