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Optimizing your business with AI and ML techniques

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  • Optimizing Retrieval-Augmented Generation (RAG) with Serverless and Sidecar Architecture

    In the world of Generative AI, Retrieval-Augmented Generation (RAG) workflows have emerged as a powerful paradigm. By combining external knowledge retrieval with generative language models, RAG bridges the gap between static AI models and dynamic, contextually rich applications. However, implementing scalable, efficient, and secure RAG pipelines presents unique challenges. Enter the architecture that leverages both…

  • Practical Business-Ready RAG: Advanced Insights into Real-World Implementation

    Unlock Business Value with Practical RAG Implementation In our previous series, we dissected the advantages of RAG (Retrieval-Augmented Generation) with a focus on its potential to mitigate hallucinations in generative models. Now, we pivot to a parallel series that takes a granular look at the RAG framework, specifically addressing the operational complexities that prevent it…