The Secret Sauce for Vector Search: Training Embedding Models

Organizations are rushing to use generative AI and vector similarity search, but many overlook the crucial step of fine-tuning embedding models. Fine-tuning improves search accuracy by tailoring models to specific business contexts, outperforming general models that can yield worse results than traditional keyword search methods. This process involves retraining model parameters with relevant data, introducing business metrics, and implementing continuous learning systems for optimal performance.