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LLMOps, Governance, and Observability: The Triad for Responsible & Effective AI

As enterprises accelerate their adoption of Large Language Models (LLMs), three foundational pillars—LLMOps, governance, and observability—are emerging as the cornerstones of responsible, scalable, and effective AI. While each serves a distinct purpose, their intersection forms a critical triad that ensures safe scaling, sustained quality, and trusted outcomes. Defining the Pillars LLMOps (Large Language Model…

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AI Virtual Employees: LiteCone Launch Transforms Insurance at AWS AI Conclave 2025

At AWS AI Conclave 2025, industry leaders and tech visionaries witnessed a significant breakthrough in AI innovation. LUMIQ introduced LiteCone, a family of Agentic AI Workers that represents a transformative development for the insurance sector. Additional sessions explored AI's evolution beyond chatbots, focusing on verticalized Small Language Models for claims processing and relationship management, with…

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Demystifying RAG for Business Leaders

Generative AI and large language models (LLMs) have transformed the way businesses harness data to derive insights and make strategic business decisions. These models possess incredible power, and services like Amazon Bedrock and Amazon SageMaker Jumpstart have further amplified these opportunities and challenges, making it easier for businesses to integrate GenAI into their operations and…

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From Hallucinations to Accuracy: Augmenting LLMs with Traditional ML Techniques

 In the previous blog, we explored the impact of Retrieval-Augmented Generation (RAG) from both a business and technical perspective, diving deep into how it can transform decision-making processes and be implemented using AWS Cloud infrastructure. However, a critical challenge that remains when using large language models (LLMs) is the issue of hallucinations—where models generate responses…

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[Updated] Reference Architecture for Agents & RAG

Background In 2023, Andreessen Horowitz published a fantastic architecture on building and deploying RAG-based LLM solutions (link here). Inspired by their work, we’ve developed and iterated on the framework to better support the integration of both RAG and Agents in modern Generative AI applications. Updated Reference Architecture for Agents & RAG Below is our Updated Reference Architecture…

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