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These Chrome features and tips for students can help you learn more easily while staying organized and focused, too. ​ 

Large language models (LLMs) have become increasingly prevalent across both consumer and enterprise applications. However, their tendency to “hallucinate” information and deliver incorrect answers with seeming confidence has created a trust problem. Think of LLMs as you would a human expert: we typically trust experts who can back up their claims with references and walk

We’re introducing Flight Deals, a new, AI-powered search tool. Plus, there’s a new way to exclude basic economy on Google Flights. ​ 

Organizations are increasingly excited about the potential of AI agents, but many find themselves stuck in what we call “proof of concept purgatory”—where promising agent prototypes struggle to make the leap to production deployment. In our conversations with customers, we’ve heard consistent challenges that block the path from experimentation to enterprise-grade deployment: “Our developers want

Google is investing an additional $9 billion in Oklahoma within the next two years in cloud and AI infrastructure. This investment supports the development of a new data… ​ 

This is a guest post co-written with Scott Likens, Ambuj Gupta, Adam Hood, Chantal Hudson, Priyanka Mukhopadhyay, Deniz Konak Ozturk, and Kevin Paul from PwC Organizations are deploying generative AI solutions while balancing accuracy, security, and compliance. In this globally competitive environment, scale matters less, speed matters more, and innovation matters most of all, according

At Amazon, our team builds Rufus, a generative AI-powered shopping assistant that serves millions of customers at immense scale. However, deploying Rufus at scale introduces significant challenges that must be carefully navigated. Rufus is powered by a custom-built large language model (LLM). As the model’s complexity increased, we prioritized developing scalable multi-node inference capabilities that

Agentic AI is revolutionizing the financial services industry through its ability to make autonomous decisions and adapt in real time, moving well beyond traditional automation. Imagine an AI assistant that can analyze quarterly earnings reports, compare them against industry expectations, and generate insights about future performance. This seemingly straightforward task involves multiple complex steps: document

AI assistants that forget what you told them 5 minutes ago aren’t very helpful. While large language models (LLMs) excel at generating human-like responses, they are fundamentally stateless—they don’t retain information between interactions. This forces developers to build custom memory systems to track conversation history, remember user preferences, and maintain context across sessions, often solving

Data analysis often presents significant challenges for business users who aren’t proficient in SQL. Traditional methods require technical expertise to query databases, leading to delayed insights and dependence on data teams. Many organizations struggle with making their data accessible to business users while maintaining the analytical capabilities of Amazon Athena. Modern AI agents are transforming