As enterprises generate and consume increasing volumes of diverse data, extracting insights from multimodal documents, like PDFs and presentations, has become a major challenge. Traditional text-only extraction and basic retrieval-augmented generation (RAG) pipelines fall short, failing to capture the full value of these complex documents. The result? Missed insights, inefficient workflows…
]]>Evaluating large language models (LLMs) and retrieval-augmented generation (RAG) systems is a complex and nuanced process, reflecting the sophisticated and multifaceted nature of these systems. Unlike traditional machine learning (ML) models, LLMs generate a wide range of diverse and often unpredictable outputs, making standard evaluation metrics insufficient. Key challenges include the…
]]>Equipping agentic AI applications with tools will usher in the next phase of AI. By enabling autonomous agents and other AI applications to fetch real-time data, perform actions, and interact with external systems, developers can bridge the gap to new, real-world use cases that significantly enhance productivity and the user experience. xpander AI, a member of the NVIDIA Inception program for…
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