Generative AI overview
Google Cloud offers a range of products and tools for the complete life cycle of building generative AI applications.
Generative AI on Vertex AI
Access Google's large generative AI models so you can test, tune, and deploy them for use in your AI-powered applications.
Gemini Quickstart
See what it's like to send requests to the Gemini API through Google Cloud's AI-ML platform, Vertex AI.
Choose infrastructure for your generative AI application
Choose the best products and tools for your use case and access the documentation you need to get started.
When to use generative AI
Identify whether generative AI, traditional AI, or a combination of both might suit your business use case.
Develop a generative AI application
Learn how to address the challenges in each stage of developing a generative AI application.
Code samples and sample applications
View code samples for popular use cases and deploy examples of generative AI applications that are secure, efficient, resilient, high-performing, and cost-effective.
Generative AI glossary
Learn about specific terms that are associated with generative AI.
Google Models on Vertex AI (Gemini, Imagen)
Discover test, customize, and deploy Google models and assets from an ML model library.
Other models in the Vertex AI Model Garden
Discover, test, customize, and deploy select OSS models and assets from an ML model library.
Text generation models via HuggingFace
Learn how to deploy HuggingFace text generation models to Vertex AI or Google Kubernetes Engine (GKE).
AI/ML orchestration on GKE
GKE efficiently orchestrates AI/ML workloads, supporting GPUs and TPUs for scalable generative AI training and serving.
GPUs on Compute Engine
Attach GPUs to VM instances to accelerate generative AI workloads on Compute Engine.
Vertex AI Studio
Design, test, and customize your prompts sent to Google's Gemini and PaLM 2 large language models (LLM).
Overview of Prompting Strategies
Learn the prompt-engineering workflow and common strategies that you can use to affect model responses.
Prompt Gallery
View example prompts and responses for specific use cases.
Vertex AI grounding
You can ground Vertex AI models with Google Search or with your own data stored in Vertex AI Search.
Ground with Google Search
Use Grounding with Google Search to connect the model to the up-to-date knowledge available on the internet.
Vector embeddings in AlloyDB
Use AlloyDB to generate and store vector embeddings, then index and query the embeddings using the pgvector extension.
Cloud SQL and pgvector
Store vector embeddings in Postgres SQL, then index and query the embeddings using the pgvector extension.
Integrating BigQuery data into your LangChain application
Use LangChain to extract data from BigQuery and enrich and ground your model's responses.
Vector embeddings in Firestore
Create vector embeddings from your Firestore data, then index and query the embeddings.
Vector embeddings in Memorystore (Redis)
Use LangChain to extract data from Memorystore and enrich and ground your model's responses.
AI Applications
Leverage Google's foundation models, search expertise, and conversational AI technologies for enterprise-grade generative AI applications.
Vertex AI Function calling
Add function calling to your model to enable actions like booking a reservation based on extracted calendar information.
Evaluate models in Vertex AI
Evaluate the performance of foundation models and your tuned generative AI models on Vertex AI.
Tune Vertex AI models
General purpose foundation models can benefit from tuning to improve their performance on specific tasks.
Cloud TPU
TPUs are Google's custom-developed ASICs used to accelerate machine learning workloads, such as training an LLM.