Mosaic AI Vector Search Reranking: Faster, Smarter RAG Retrieval
For many organizations deploying artificial intelligence agents, the primary hurdle isn’t the sophistication of the AI model itself, but rather the quality of the information it receives. If an agent fails to retrieve the most pertinent context, even the most advanced large language models can miss critical details, leading to incomplete or inaccurate responses.
Addressing this challenge, Mosaic AI Vector Search is introducing a new reranking feature, now available in Public Preview. This enhancement promises to significantly boost retrieval accuracy, demonstrating an average improvement of 15 percentage points on internal enterprise benchmarks with the simple flip of a single parameter. The result is a marked improvement in answer quality, more robust reasoning capabilities, and consistently better performance from AI agents, all without requiring additional infrastructure or complex setup.
Reranking is a sophisticated technique designed to elevate agent performance by ensuring the most relevant data is presented for a given task. While vector databases are exceptionally efficient at rapidly sifting through millions of potential documents to find broadly relevant candidates, reranking applies a deeper, more nuanced contextual understanding. This second stage reorders the initial results, bringing the most semantically pertinent information to the very top. This two-stage approach—combining rapid initial retrieval with intelligent reordering—has become indispensable for modern retrieval augmented generation (RAG) agent systems, where the precision and quality of responses are paramount.
The decision to integrate reranking stemmed directly from customer feedback, which highlighted two recurring issues. First, AI agents frequently struggled to pinpoint critical context buried within vast, unstructured datasets. The truly “right” piece of information often wasn’t among the top results returned by a standard vector database. Second, while some organizations attempted to build their own reranking systems to enhance agent quality, these bespoke solutions proved time-consuming to develop—often taking weeks—and required substantial ongoing maintenance. By embedding reranking directly into Vector Search, organizations can now leverage their governed enterprise data to surface the most relevant information without incurring additional engineering overhead.
The impact of this innovation is already evident. David Brady, Senior Director at G3 Enterprises, noted a transformative change in their Lexi chatbot: “The reranker feature helped elevate our Lexi chatbot from functioning like a high school student to performing like a law school graduate. We have seen transformative gains in how our systems understand, reason over, and generate content from legal documents—unlocking insights that were previously buried in unstructured data.”
Databricks’ research team achieved this breakthrough by developing a novel compound AI system specifically tailored for agent workloads. On internal enterprise benchmarks, this system successfully retrieves the correct answer within its top 10 results 89% of the time (a metric known as recall@10). This represents a substantial 15-point improvement over the previous baseline of 74% and is 10 points higher than leading cloud alternatives, which typically achieve 79%. Crucially, this enhanced quality is delivered with remarkable speed, with latencies as low as 1.5 seconds. In contrast, many contemporary systems often require several seconds, or even minutes, to return high-quality answers. The system is optimized to rerank 50 results in as little as 1.5 seconds, ensuring that sophisticated retrieval strategies do not compromise user experience.
Enabling this enterprise-grade reranking capability is remarkably straightforward, taking minutes rather than weeks. Traditionally, teams would dedicate significant time to researching models, deploying infrastructure, and writing custom logic. With Vector Search, activating reranking requires adding just one additional parameter to a query, instantly boosting retrieval quality for AI agents. This eliminates the need to manage separate model serving endpoints, maintain custom wrappers, or fine-tune complex configurations. Furthermore, users can specify multiple columns for reranking, providing the system with access to rich metadata beyond just the main text, such as contract summaries or category information, to further enhance contextual understanding and result relevance.
Reranking is particularly beneficial for any RAG agent use case where the right answer is present within a broader set of initial results but struggles to appear among the top few. In technical terms, this means customers with low recall@10 but high recall@50—where the correct information is retrieved within the top 50 results but not consistently within the top 10—will likely see the most significant quality gains. This new feature represents a significant step forward in making AI agents more accurate, efficient, and ultimately, more valuable for enterprise applications.