Building Better Product Search with Fine-Tuned Retrieval Models

About Event Generic embedding models get you "kind of similar." Real product search needs the right brand, size, color, and variant. That gap is where fine-tuned retrieval models earn their keep. In this hands-on workshop, you'll build a product search lab around a custom fine-tuned retrieval model trained on realistic e-commerce data. You'll see where the baseline embedding model breaks, what fine-tuning fixes, and how to measure the lift with real relevance metrics. You'll also see how the fine-tuned model fits alongside sparse and hybrid search in a production retrieval stack. We'll dig into the failure modes generic models can't fix: semantically close but commercially wrong results, missed attributes, weak exact-match behavior, irrelevant substitutes, and bad ranking. You'll learn how hard negatives and domain-specific training data drive most of the quality gains, and how to tell whether a fine-tuned model is actually pulling its weight in production. The same model also powers "similar products" and preference-based recommendations, so we'll cover that path too. No pre-work…