컬렉션을 사용해 정리하기
내 환경설정을 기준으로 콘텐츠를 저장하고 분류하세요.
생성형 AI 개요
Google Cloud 는 생성형 AI 애플리케이션을 빌드하는 전체 수명 주기에 걸쳐 사용할 수 있는 다양한 제품과 도구를 제공합니다.
모델 탐색 및 호스팅
Google Cloud 는 Gemini를 포함하여 Vertex AI를 통해 최신 파운데이션 모델 집합을 제공합니다. 또한 서드 파티 모델을 Vertex AI Model Garden 또는 GKE나 Compute Engine의 자체 호스팅에 배포할 수 있습니다.
프롬프트 설계 및 엔지니어링
프롬프트 설계는 프롬프트 및 응답 쌍을 작성하여 언어 모델에 추가 컨텍스트와 안내를 제공하는 프로세스입니다. 프롬프트를 작성한 후에는 사전 학습을 위한 프롬프트 데이터 세트로 모델에 공급합니다. 모델에서 예측을 제공하면 기본 제공되는 안내로 응답합니다.
그라운딩 및 RAG
그라운딩은 AI 모델을 데이터 소스에 연결하여 응답 정확성을 향상시키고 할루시네이션을 줄입니다. 일반적인 그라운딩 기법인 RAG는 관련 정보를 검색하여 모델 프롬프트에 추가하므로 출력은 사실과 최신 정보를 기반으로 합니다.
에이전트 및 함수 호출
에이전트를 사용하면 대화형 사용자 인터페이스를 쉽게 설계하고 모바일 앱에 통합할 수 있으며 함수 호출은 모델 기능을 확장합니다.
모델 맞춤설정 및 학습
특정 용어로 언어 모델 학습과 같은 전문화된 태스크를 수행하려면 프롬프트 설계나 그라운딩만으로 수행하는 경우보다 더 많은 학습이 필요할 수 있습니다. 이 시나리오에서는 모델 조정을 사용하여 성능을 향상시키거나 자체 모델을 학습시킬 수 있습니다.
LangChain 설정
LangChain은 프롬프트에 컨텍스트를 빌드하고 모델 응답에 반응할 수 있는 생성형 AI 앱용 오픈소스 프레임워크입니다.
달리 명시되지 않는 한 이 페이지의 콘텐츠에는 Creative Commons Attribution 4.0 라이선스에 따라 라이선스가 부여되며, 코드 샘플에는 Apache 2.0 라이선스에 따라 라이선스가 부여됩니다. 자세한 내용은 Google Developers 사이트 정책을 참조하세요. 자바는 Oracle 및/또는 Oracle 계열사의 등록 상표입니다.
최종 업데이트: 2025-05-29(UTC)
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-05-29(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."]]
