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  • Manager’s Guide: Enabling Generative AI Enterprise Deployment with Retrieval Augmented Generation (RAG)

    This management guide outlines how businesses can deploy generative AI effectively, focusing on Retrieval-Augmented Generation (RAG) to integrate private data for tailored, context-rich responses. It explains key principles, including retrieval, generation, embedding, and hybrid search, highlighting their role in improving relevance and efficiency. The guide addresses latency, cost, scalability, and security challenges, offering optimization strategies and governance best practices. It proposes a phased approach to adoption, providing actionable steps for successfully integrating RAG and generative AI into business operations.

  • Benchmark Summary: Elasticsearch VS Vespa Performance Comparison

    In November 2024, Vespa Engineering conducted an in-depth benchmark comparison of Elasticsearch and Vespa. The key findings of this benchmark are presented in this 6-page report. The full 84-page report is available below.

  • Benchmark Report: Elasticsearch vs Vespa Performance Comparison

    In November 2024, Vespa Engineering conducted an in-depth benchmark comparison of Elasticsearch and Vespa, presented in this comprehensive 84-page report. The report details the methodology used to ensure a fair and thorough evaluation, provides an in-depth analysis of both products’ architectures, and concludes with step-by-step instructions for reproducing the benchmark in your own environment. This ensures both transparency and practical insights for your assessment.

  • Manager’s Guide: Simplifying AI Application Development and Deployment in Fintech with Vespa.ai

    Discover how GenAI and traditional ML are transforming fintech and banking, driving growth, efficiency, and customer loyalty. From personalized insights and fraud alerts in fintech to streamlined compliance, risk management, and credit scoring in traditional banking, these technologies are reshaping the industry. Learn how Vespa enables the seamless development and deployment of AI applications, empowering enterprises to harness automation, enhance decision-making, and boost productivity.