Large Language Models (LLMs)
Large Language Models powered by world-class Google AI
Google Cloud brings innovations developed and tested by Google DeepMind to our enterprise-ready AI platform so customers can start using them to build and deliver generative AI capabilities today — not tomorrow.
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Overview
What is a large language model (LLM)?
A large language model (LLM) is a statistical language model, trained on a massive amount of data, that can be used to generate and translate text and other content, and perform other natural language processing (NLP) tasks.
LLMs are typically based on deep learning architectures, such as the Transformer developed by Google in 2017, and can be trained on billions of text and other content.
What are some examples of popular foundation models?
Agent Platform offers access to Gemini, a multimodal model from Google DeepMind. Gemini is capable of understanding virtually any input, combining different types of information, and generating almost any output. Prompt and test in Agent Platform with Gemini, using text, images, video, or code. Using Gemini’s advanced reasoning and state-of-the-art generation capabilities, developers can try sample prompts for extracting text from images, converting image text to JSON, and even generate answers about uploaded images to build next-gen AI applications.
What are the use cases for large language models?
Text-driven LLMs are used for a variety of natural language processing tasks, including text generation, machine translation, text summarization, question answering, and creating chatbots that can hold conversations with humans.
LLMs can also be trained on other types of data, including code, images, audio, video, and more. Google AI's Veo, Imagen and Chirp are examples of such models that will spawn new applications and help create solutions to the world’s most challenging problems.
What are the benefits of large language models?
LLMs are pre-trained on a massive amount of data. They are extremely flexible because they can be trained to perform a variety of tasks, such as text generation, summarization, and translation. They are also scalable because they can be fine-tuned to specific tasks, which can improve their performance.
What large language model services does Google Cloud offer?
Agent Studio on Gemini Enterprise Agent Platform: Gives you access to over 200 large generative AI models so you can test, tune, and deploy them for use in your AI-powered applications.
Gemini Enterprise app: A collaborative workspace that enables employees to discover create, share, and run AI agents in a single, secure environment that connects all your enterprise data. The Gemini Enterprise app is built on the Agent Platform meaning all agent governance, security and identity capabilities come as standard with the app.
Gemini Enterprise for Customer Experience: Intelligent Contact Center solution which includes CX Agent Studio our conversational AI platform with both intent-based and LLM capabilities.
How It Works
LLMs work by using a massive amount of text data to train a neural network. This neural network is then used to generate text, translate text, or perform other tasks. The more data that is used to train the neural network, the better and more accurate it will be at performing its task.
Google Cloud developed products based on its LLM technologies, catering to a wide variety of use cases you can explore in the Common Uses section below.
LLMs work by using a massive amount of text data to train a neural network. This neural network is then used to generate text, translate text, or perform other tasks. The more data that is used to train the neural network, the better and more accurate it will be at performing its task.
Google Cloud developed products based on its LLM technologies, catering to a wide variety of use cases you can explore in the Common Uses section below.
AI Agents
Build your own AI agent
In this hands-on codelab series, you'll embark on an exciting journey to create your very own intelligent AI agent using Google's Agent Development Kit (ADK).
We'll start with the absolute essentials, guiding you through setting up your development environment and crafting a foundational conversational agent. By the end of this codelab, you'll have built your first interactive AI, ready to be expanded upon in subsequent parts of this series as we transform it into a sophisticated Multi-Agent System (MAS).
How-tos
Build your own AI agent
In this hands-on codelab series, you'll embark on an exciting journey to create your very own intelligent AI agent using Google's Agent Development Kit (ADK).
We'll start with the absolute essentials, guiding you through setting up your development environment and crafting a foundational conversational agent. By the end of this codelab, you'll have built your first interactive AI, ready to be expanded upon in subsequent parts of this series as we transform it into a sophisticated Multi-Agent System (MAS).
Research and information discovery
Search across your business data with a single prompt
Extract and summarize valuable information from complex documents, such as research papers, third-party news services, and financial reports—with a click of a button. Securely connect Gemini Enterprise app to Google Drive, Microsoft OneDrive, Sharepoint, HubSpot, Jira, and more in just a few clicks to ground its output in your business data.
How-tos
Search across your business data with a single prompt
Extract and summarize valuable information from complex documents, such as research papers, third-party news services, and financial reports—with a click of a button. Securely connect Gemini Enterprise app to Google Drive, Microsoft OneDrive, Sharepoint, HubSpot, Jira, and more in just a few clicks to ground its output in your business data.
Document summarization
Process and summarize large documents using Agent Platform LLMs
With Generative AI Document Summarization, deploy a one-click solution that helps detect text in raw files and automate document summaries. The solution establishes a pipeline that uses Cloud Vision Optical Character Recognition (OCR) to extract text from uploaded PDF documents in Cloud Storage, creates a summary from the extracted text with Agent Platform, and stores the searchable summary in a BigQuery database.
How-tos
Process and summarize large documents using Agent Platform LLMs
With Generative AI Document Summarization, deploy a one-click solution that helps detect text in raw files and automate document summaries. The solution establishes a pipeline that uses Cloud Vision Optical Character Recognition (OCR) to extract text from uploaded PDF documents in Cloud Storage, creates a summary from the extracted text with Agent Platform, and stores the searchable summary in a BigQuery database.
Agents across the customer lifecycle
Connect with customers using natural, real-time voice
With Gemini Enterprise for Customer Experience (CX), your business can build and manage agents that speak with a natural, human-like voice across the entire customer lifecycle, from product discovery to post-purchase resolution. Deploy agents that use complex reasoning to understand intent and provide high-quality, conversational support to deliver personalized service to your customers taking into account their preferences and consent.
How-tos
Connect with customers using natural, real-time voice
With Gemini Enterprise for Customer Experience (CX), your business can build and manage agents that speak with a natural, human-like voice across the entire customer lifecycle, from product discovery to post-purchase resolution. Deploy agents that use complex reasoning to understand intent and provide high-quality, conversational support to deliver personalized service to your customers taking into account their preferences and consent.
