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Generative Search: Practical Advice for Retrieval Augmented Generation (RAG)
Sam Partee discusses Vector embeddings in LLMs, a tool capable of capturing the essence of unstructured data used by LLMs to gain access to a wealth of contextually relevant knowledge.
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Defensible Moats: Unlocking Enterprise Value with Large Language Models
Nischal HP discusses risk mitigation, environmental, social, and governance (ESG) framework implementation to achieve sustainability goals, strategic procurement, spend analytics, data compliance.
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When AIOps Meets MLOps: What it Takes to Deploy ML Models at Scale
Ghida Ibrahim introduces the concept of AIOps referring to using AI and data-driven tooling to provision, manage and scale distributed IT infra.
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Reach Next-Level Autonomy with LLM-Based AI Agents
Tingyi Li discusses the AI Agent, exploring how it extends the frontiers of Generative AI applications and leads to next-level autonomy in combination with enterprise data.
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Retrieval-Augmented Generation (RAG) Patterns and Best Practices
Jay Alammar discusses the common schematics of RAG systems and tips on how to improve them.
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Large Language Models for Code: Exploring the Landscape, Opportunities, and Challenges
Loubna Ben Allal discusses Large Language Models (LLMs), exploring the current developments of these models, how they are trained, and how they can be leveraged with custom codebases.
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A Bicycle for the (AI) Mind: GPT-4 + Tools
Sherwin Wu and Atty Eleti discuss how to use the OpenAI API to integrate large language models into your application, and extend GPT’s capabilities by connecting it to the external world via APIs.
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