AI generativa
Documentazione e risorse per creare e implementare applicazioni di AI generativa con Google Cloud strumenti e prodotti.
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Flusso di sviluppo dell'AI generativa
Esplorazione e hosting del modello
Google Cloud fornisce un insieme di modelli di base all'avanguardia tramite Vertex AI, tra cui Gemini. Puoi anche eseguire il deployment di un modello di terze parti in Vertex AI Model Garden o eseguire l'hosting autonomo su GKE o Compute Engine.
Progettazione e ingegneria dei prompt
La progettazione dei prompt è il processo di creazione di coppie di prompt e risposte per fornire ai modelli linguistici contesto e istruzioni aggiuntivi. Dopo aver creato i prompt, li fornisci al modello come set di dati di prompt per il pre-addestramento. Quando un modello fornisce previsioni, risponde con le istruzioni integrate.
Grounding e RAG
Il grounding collega i modelli di AI alle origini dati per migliorare l'accuratezza delle risposte e ridurre le allucinazioni. La RAG, una tecnica di grounding comune, cerca informazioni pertinenti e le aggiunge al prompt del modello, garantendo che l'output si basi su fatti e informazioni aggiornate.
Agenti e chiamata di funzione
Gli agenti semplificano la progettazione e l'integrazione di un'interfaccia utente conversazionale nella tua app mobile, mentre la chiamata di funzioni estende le funzionalità di un modello.
Personalizzazione e addestramento dei modelli
Attività specializzate, come l'addestramento di un modello linguistico su una terminologia specifica, potrebbero richiedere un addestramento più approfondito rispetto a quello che puoi fare solo con la progettazione o la fondatezza dei prompt. In questo scenario, puoi utilizzare l'ottimizzazione del modello per migliorare le prestazioni o addestrare il tuo modello.
Salvo quando diversamente specificato, i contenuti di questa pagina sono concessi in base alla licenza Creative Commons Attribution 4.0, mentre gli esempi di codice sono concessi in base alla licenza Apache 2.0. Per ulteriori dettagli, consulta le norme del sito di Google Developers. Java è un marchio registrato di Oracle e/o delle sue consociate.
Ultimo aggiornamento 2025-09-04 UTC.
[[["Facile da capire","easyToUnderstand","thumb-up"],["Il problema è stato risolto","solvedMyProblem","thumb-up"],["Altra","otherUp","thumb-up"]],[["Difficile da capire","hardToUnderstand","thumb-down"],["Informazioni o codice di esempio errati","incorrectInformationOrSampleCode","thumb-down"],["Mancano le informazioni o gli esempi di cui ho bisogno","missingTheInformationSamplesINeed","thumb-down"],["Problema di traduzione","translationIssue","thumb-down"],["Altra","otherDown","thumb-down"]],["Ultimo aggiornamento 2025-09-04 UTC."],[[["\u003cp\u003eGoogle Cloud provides comprehensive tools and products for every stage of building generative AI applications, from model exploration to deployment.\u003c/p\u003e\n"],["\u003cp\u003eVertex AI allows users to access, test, tune, and deploy Google's large generative AI models, including Gemini, for use in AI-powered applications.\u003c/p\u003e\n"],["\u003cp\u003ePrompt design and engineering, including using Vertex AI Studio, are crucial for shaping model responses and optimizing their effectiveness.\u003c/p\u003e\n"],["\u003cp\u003eGrounding techniques, like RAG, connect AI models to data sources to improve accuracy and reduce hallucinations, using tools like Google Search, AlloyDB, Cloud SQL, and more.\u003c/p\u003e\n"],["\u003cp\u003eDevelopers can customize and train models, using tools like Cloud TPU, and evaluate performance with Vertex AI to enhance model effectiveness on specialized tasks.\u003c/p\u003e\n"]]],[],null,["# Generative AI\n=============\n\nDocumentation and resources for building and implementing generative AI\napplications with Google Cloud tools and products.\n[Get started for free](https://console.cloud.google.com/freetrial) \n\n#### Start your proof of concept with $300 in free credit\n\n- Get access to Gemini 2.0 Flash Thinking\n- Free monthly usage of popular products, including AI APIs and BigQuery\n- No automatic charges, no commitment \n[View free product offers](/free/docs/free-cloud-features#free-tier) \n\n#### Keep exploring with 20+ always-free products\n\n\nAccess 20+ free products for common use cases, including AI APIs, VMs, data warehouses,\nand more.\n\nLearn about building generative AI applications\n-----------------------------------------------\n\n### [Generative AI on Vertex AI](/vertex-ai/generative-ai/docs/overview)\n\nAccess Google's large generative AI models so you can test, tune, and deploy them for use in your AI-powered applications. \n\n### [Gemini Quickstart](/vertex-ai/generative-ai/docs/start/quickstarts/quickstart-multimodal)\n\nSee what it's like to send requests to the Gemini API through Google Cloud's AI-ML platform, Vertex AI. \n\n### [AI/ML orchestration on GKE](/kubernetes-engine/docs/integrations/ai-infra)\n\nLeverage the power of GKE as a customizable AI/ML platform featuring high performance, cost effective serving and training with industry-leading scale and flexible infrastructure options. \n\n### [When to use generative AI](/docs/ai-ml/generative-ai/generative-ai-or-traditional-ai)\n\nIdentify whether generative AI, traditional AI, or a combination of both might suit your business use case. \n\n### [Develop a generative AI application](/docs/ai-ml/generative-ai/develop-generative-ai-application)\n\nLearn how to address the challenges in each stage of developing a generative AI application. \n\n### [Code samples and sample applications](/docs/generative-ai/code-samples)\n\nView code samples for popular use cases and deploy examples of generative AI applications that are secure, efficient, resilient, high-performing, and cost-effective. \n\n### [Generative AI glossary](/docs/generative-ai/glossary)\n\nLearn about specific terms that are associated with generative AI.\n\nGen AI tools\n------------\n\nGen AI development flow\n-----------------------\n\nModel exploration and hosting\n-----------------------------\n\nGoogle Cloud provides a set of state-of-the-art foundation models through Vertex AI, including Gemini. You can also deploy a third-party model to either Vertex AI Model Garden or self-host on GKE or Compute Engine. \n\n### [Google Models on Vertex AI (Gemini, Imagen)](/vertex-ai/generative-ai/docs/learn/models)\n\nDiscover test, customize, and deploy Google models and assets from an ML model library. \n\n### [Other models in the Vertex AI Model Garden](/vertex-ai/generative-ai/docs/model-garden/explore-models)\n\nDiscover, test, customize, and deploy select OSS models and assets from an ML model library. \n\n### [Text generation models via HuggingFace](/vertex-ai/generative-ai/docs/open-models/use-hugging-face-models)\n\nLearn how to deploy HuggingFace text generation models to Vertex AI or Google Kubernetes Engine (GKE). \n\n### [GPUs on Compute Engine](/compute/docs/gpus/about-gpus)\n\nAttach GPUs to VM instances to accelerate generative AI workloads on Compute Engine.\n\nPrompt design and engineering\n-----------------------------\n\nPrompt design is the process of authoring prompt and response pairs to give language models additional context and instructions. After you author prompts, you feed them to the model as a prompt dataset for pretraining. When a model serves predictions, it responds with your instructions built in. \n\n### [Vertex AI Studio](/vertex-ai/generative-ai/docs/start/quickstarts/quickstart)\n\nDesign, test, and customize your prompts sent to Google's Gemini and PaLM 2 large language models (LLM). \n\n### [Overview of Prompting Strategies](/vertex-ai/generative-ai/docs/learn/prompts/prompt-design-strategies)\n\nLearn the prompt-engineering workflow and common strategies that you can use to affect model responses. \n\n### [Prompt Gallery](/vertex-ai/generative-ai/docs/prompt-gallery)\n\nView example prompts and responses for specific use cases.\n\nGrounding and RAG\n-----------------\n\n*Grounding* connects AI models to data sources to improve the accuracy of responses and reduce hallucinations. *RAG*, a common grounding technique, searches for relevant information and adds it to the model's prompt, ensuring output is based on facts and up-to-date information. \n\n### [Vertex AI grounding](/vertex-ai/generative-ai/docs/grounding/overview)\n\nYou can ground Vertex AI models with Google Search or with your own data stored in Vertex AI Search. \n\n### [Ground with Google Search](/vertex-ai/generative-ai/docs/multimodal/ground-gemini#web-ground-gemini)\n\nUse Grounding with Google Search to connect the model to the up-to-date knowledge available on the internet. \n\n### [Vector embeddings in AlloyDB](/alloydb/docs/ai/work-with-embeddings)\n\nUse AlloyDB to generate and store vector embeddings, then index and query the embeddings using the pgvector extension. \n\n### [Cloud SQL and pgvector](https://github.com/pgvector/pgvector?tab=readme-ov-file#pgvector)\n\nStore vector embeddings in Postgres SQL, then index and query the embeddings using the pgvector extension. \n\n### [Integrating BigQuery data into your LangChain application](https://cloud.google.com/blog/products/ai-machine-learning/open-source-framework-for-connecting-llms-to-your-data)\n\nUse LangChain to extract data from BigQuery and enrich and ground your model's responses. \n[description](/firestore/docs/vector-search) \n\n### [Vector embeddings in Firestore](/firestore/docs/vector-search)\n\nCreate vector embeddings from your Firestore data, then index and query the embeddings. \n\n### [Vector embeddings in Memorystore (Redis)](/memorystore/docs/redis/about-vector-search)\n\nUse LangChain to extract data from Memorystore and enrich and ground your model's responses.\n\nAgents and function calling\n---------------------------\n\nAgents make it easy to design and integrate a conversational user interface into your mobile app, while function calling extends the capabilities of a model. \n\n### [AI Applications](/generative-ai-app-builder/docs/introduction)\n\nLeverage Google's foundation models, search expertise, and conversational AI technologies for enterprise-grade generative AI applications. \n\n### [Vertex AI Function calling](/vertex-ai/generative-ai/docs/multimodal/function-calling)\n\nAdd function calling to your model to enable actions like booking a reservation based on extracted calendar information.\n\nModel customization and training\n--------------------------------\n\nSpecialized tasks, such as training a language model on specific terminology, might require more training than you can do with prompt design or grounding alone. In that scenario, you can use model tuning to improve performance, or train your own model. \n\n### [Evaluate models in Vertex AI](/vertex-ai/generative-ai/docs/models/evaluation-overview)\n\nEvaluate the performance of foundation models and your tuned generative AI models on Vertex AI. \n\n### [Tune Vertex AI models](/vertex-ai/generative-ai/docs/models/tune-models)\n\nGeneral purpose foundation models can benefit from tuning to improve their performance on specific tasks. \n\n### [Cloud TPU](/tpu/docs)\n\nTPUs are Google's custom-developed ASICs used to accelerate machine learning workloads, such as training an LLM.\n\nRelated guides and sites\n------------------------\n\n[description](/architecture/gen-ai-rag-vertex-ai-vector-search) \nIntermediate\n\n### [Infrastructure for a RAG-capable generative AI application using Vertex AI and Vector Search](/architecture/gen-ai-rag-vertex-ai-vector-search)\n\nReference architecture for a RAG-capable generative AI application using Vertex AI and Vector Search. \n[description](/architecture/rag-capable-gen-ai-app-using-vertex-ai) \nIntermediate\n\n### [Infrastructure for a RAG-capable generative AI application using Vertex AI and AlloyDB for PostgreSQL](/architecture/rag-capable-gen-ai-app-using-vertex-ai)\n\nReference architecture for a RAG-capable generative AI application using Vertex AI and AlloyDB for PostgreSQL. \n[description](/architecture/rag-capable-gen-ai-app-using-gke) \nIntermediate\n\n### [Infrastructure for a RAG-capable generative AI application using GKE and Cloud SQL](/architecture/rag-capable-gen-ai-app-using-gke)\n\nReference architecture for a RAG-capable generative AI application using GKE, Cloud SQL, and open source tools like Ray, Hugging Face, and LangChain.\n\nStart building\n--------------\n\n### Set up your development environment for Google Cloud\n\n- [C# and .NET](/dotnet/docs/setup)\n- [C++](/cpp/docs/setup)\n- [Go](/go/docs/setup)\n- [Java](/java/docs/setup)\n- [JavaScript and Node.js](/nodejs/docs/setup)\n- [Python](/python/docs/setup)\n- [Ruby](/ruby/docs/setup)\n\n### Set up LangChain\n\nLangChain is an open source framework for generative AI apps that allows you to build context into your prompts, and take action based on the model's response.\n\n- [Python (LangChain)](https://python.langchain.com/docs/integrations/llms/google_vertex_ai_palm)\n- [JavaScript (LangChain.js)](https://js.langchain.com/docs/integrations/platforms/google)\n- [Java (LangChain4j)](https://docs.langchain4j.dev/integrations/language-models/google-palm/)\n- [Go (LangChainGo)](https://tmc.github.io/langchaingo/docs/)\n\n### View code samples and deploy sample applications\n\nView [code samples for popular use cases and deploy examples of generative AI applications](/docs/generative-ai/code-samples) that are secure, efficient, resilient, high-performing, and cost-effective."]]
