Model adaptation is an exercise in data and evaluation.

The goal was to adapt a pretrained language model to specialized data and determine whether training produced a meaningful improvement over a general-purpose baseline. The project covered the complete path from raw examples to evaluated model behavior.

I collected and cleaned domain-relevant text, removed duplicates and low-quality samples, normalized inconsistent formatting and converted examples into the structure expected by the selected model. Dataset quality was treated as an engineering problem rather than an afterthought.

Before training, representative prompts were run against the base model. These examples provided a reference for relevance, terminology, consistency and common failure modes, making later comparisons more disciplined.

The pretrained model was continued on the curated dataset while monitoring training and validation behavior. Learning rate, batch size and dataset composition were iterated carefully to improve specialization without simply memorizing examples or degrading general capability.

Fine-tuning is not automatically the right solution. This work reinforced the need to compare it with prompting and retrieval-augmented generation across quality, latency, cost, maintainability and how frequently the underlying knowledge changes.

Model adaptation is primarily an exercise in data quality and evaluation discipline. The training run matters, but the strongest gains come from knowing what behavior should change, measuring it consistently and understanding the remaining failures.