How Hybrid Search and Rerankers Solve the GenAI Accuracy Challenge

The biggest problem with most generative AI apps isn't the model, but rather the data layer, which can lead to vague or misleading results if the retrieval pipeline can't deliver accurate context. Most search architectures weren't designed for GenAI and rely on vector search alone, which is not enough to achieve accuracy. To build GenAI apps that perform in production, a hybrid approach is necessary, combining vector search with reranking techniques that evaluate retrieved content against user queries. This approach improves accuracy by up to 45 percent compared to vector-only search and provides a more accurate context for domains like healthcare, legal, and customer support.

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