The effectiveness of vector similarity search hinges on the quality of vector embeddings, which organizations are often neglecting in favor of other aspects of generative AI.
Organizations often overlook fine-tuning embedding models, which is crucial for optimizing results, leading to suboptimal performance compared to traditional search algorithms.
General embedding models may produce inferior search results compared to traditional methods like BM25, which is why fine-tuning specific to the business is important.
Fine-tuning embeddings can be done quickly, allowing organizations to improve the accuracy of their search results significantly by adapting models to their specific contexts and tasks.
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