How Google Uses Retrieval Augmented Generation
7 engineering articles about Retrieval Augmented Generation from Google's engineering team
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The article discusses the advancements in on-device AI powered by MediaTek's Neural Processing Unit (NPU) and the introduction of the LiteRT NeuroPilot Accelerator.
Lu Wang, Arian Arfaian, Luke Boyer
10 min read
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The article discusses the launch of the Google AI Edge Gallery app, which now includes audio capabilities and is available on Google Play.
Alice Zheng, Na Li
3 min read
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This article discusses the integration of Google's EmbeddingGemma model with Google Cloud's Dataflow to create a scalable embedding pipeline for AI applications.
Danny McCormick, Ian Ballantyne, Olivier Lacombe
5 min read
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EmbeddingGemma is an innovative open embedding model designed for on-device AI applications, featuring 308 million parameters for efficient performance.
Min Choi, Sahil Dua, Alice Lisak
5 min read
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The article discusses the expansion of Google's AI Edge platform to support on-device small language models (SLMs) with multimodal capabilities, including the introduction of the Gemma 3 and Gemma ...
Mark Sherwood, Matthew Chan, Marissa Ikonomidis
6 min read
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The article discusses the Vertex AI RAG Engine, a tool designed to help developers build grounded generative AI applications by addressing challenges like hallucinations and outdated knowledge.
Crispin Velez, Holt Skinner
6 min read
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The article discusses Firebase Demo Day 2024, showcasing how to build and run AI-powered applications using Firebase products like Firebase Genkit, Vertex AI, and Firebase App Hosting.
Yasmin Gehman
4 min read
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