Build a Domain-Specific Embedding Model in Under a Day

General-purpose embedding models often fail to capture the nuances of specialized data, hindering the performance of Retrieval-Augmented Generation (RAG) systems. This guide details a process to fine-tune a domain-specific embedding model on a single GPU in less than a day without manual data labeling. The method involves synthetically generating question-answer pairs from documents, mining hard negatives for effective contrastive training, and incorporating multi-hop queries. The complete workflow covers data generation, model fine-tuning, performance evaluation, and deployment using tools like NVIDIA NeMo and NIM.