Search for tag: "rag"

Data Integration for RAG

IBM Data Integration for Unstructured Data will empower data teams to ingest, transform, and pre-process their unstructured data, enabling Retrieval Augmented Generation (RAG) use cases. Sign up for…

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Watsonx.ai - AutoAI for RAG

Watch the demo about AutoAI for RAG feature in watsonx.ai, to accelerate the design and deployment of an optimized, production-quality, Retrieval-augmented generation (RAG) pattern based on…

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RAG vs. Fine Tuning

Join Cedric Clyburn as he explores the differences and use cases of Retrieval Augmented Generation (RAG) and fine-tuning in enhancing the capabilities of large language models. The video covers the…

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5分でわかるwatsonx活用 watsonx Assistantの活用編

属人的になりがちで、担当者の負担も大きい問い合わせ対応。watsonx Assistantのチャット機能と、生成AI(watsonx.ai)や検索(Watson Discovery)を組み合わせたRAGアーキテクチャーで、様々な問い合わせにも正確に回答できるチャットボット・ソリューションを紹介します。

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What are AI Agents?

In this video, Maya Murad explores the evolution of AI agents and their pivotal role in revolutionizing AI systems. From monolithic models to compound AI systems, discover how AI agents integrate…

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5 Generative AI Capabilities for Call Center Dashboards

Have you experienced frustrating interactions when calling customer care? In this video, Sai Gorti and Sharath Prasad discuss how you can say goodbye to long waits and repetitive explanations.…

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Watsonx.data: The data lakehouse for generative AI

Watch the demo to see how watsonx.data gives you the power to access and unify data across disparate data sources, to store data for AI models and applications, and to store vectorized embeddings for…

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What is Retrieval-Augmented Generation (RAG)?

Large language models usually give great answers, but because they're limited to the training data used to create the model, over time they can become incomplete--or worse, generate answers that…

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What is Retrieval-Augmented Generation (RAG)

IBM video explaining Retrieval-Augmented Generation (RAG)

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