模型幻觉、依据和真实性。Gemini 模型可能缺乏对真实知识、物理属性或准确理解的依据和真实性。此限制可能导致模型幻觉,即 Gemini for Google Cloud 可能会生成听起来很合理的输出,但实际上不正确、不相关、不当或无意义。幻觉还可能包括编造指向不存在且从未存在过的网页的链接。如需了解详情,请参阅为 Gemini for Google Cloud撰写更好的提示。
数据质量和调优。为产品在 Gemini for Google Cloud中输入的提示数据的质量、准确率和偏差可能会对其性能产生重大影响。如果用户输入的提示不准确或不正确,Gemini for Google Cloud可能会返回不理想或错误的回答。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC):2025-08-19。"],[[["\u003cp\u003eGemini for Google Cloud is designed with Google's AI principles to leverage the capabilities of large language models while mitigating potential risks like generating factually incorrect or inappropriate content.\u003c/p\u003e\n"],["\u003cp\u003eLimitations of Gemini for Google Cloud include encountering edge cases, generating outputs that are factually incorrect, and being sensitive to the quality and bias of the prompt data entered by users.\u003c/p\u003e\n"],["\u003cp\u003eGemini models can amplify biases from their training data, and have varying language quality based on the prevalence of a language or dialect in the training data, with a focus on fairness evaluations in American English.\u003c/p\u003e\n"],["\u003cp\u003eThe models can lack depth in highly specialized domains, providing superficial information, and it is not context aware of specific user environments in the Google Cloud console, limiting its ability to answer environment-specific questions.\u003c/p\u003e\n"],["\u003cp\u003eGemini for Google Cloud incorporates safety and toxicity filtering to block harmful content, ensuring responses align with Google's Acceptable Use Policy.\u003c/p\u003e\n"]]],[],null,["# Gemini for Google Cloud and responsible AI\n\nThis document describes how Gemini for Google Cloud is designed in view of the\ncapabilities, limitations, and risks that are associated with generative AI.\n\nCapabilities and risks of large language models\n-----------------------------------------------\n\nLarge language models (LLMs) can perform many useful tasks such as the\nfollowing:\n\n- Translate language.\n- Summarize text.\n- Generate code and creative writing.\n- Power chatbots and virtual assistants.\n- Complement search engines and recommendation systems.\n\nAt the same time, the evolving technical capabilities of LLMs create the\npotential for misapplication, misuse, and unintended or unforeseen consequences.\n\nLLMs can generate output that you don't expect, including text that's offensive,\ninsensitive, or factually incorrect. Because LLMs are incredibly versatile, it\ncan be difficult to predict exactly what kinds of unintended or unforeseen\noutputs they might produce.\n\nGiven these risks and complexities, Gemini for Google Cloud is designed with\n[Google's AI principles](https://ai.google/responsibility/principles/) in\nmind. However, it's important for users to understand some of the limitations of\nGemini for Google Cloud to work safely and responsibly.\n\nGemini for Google Cloud limitations\n-----------------------------------\n\nSome of the limitations that you might encounter using Gemini\nfor Google Cloud include (but aren't limited to) the following:\n\n- **Edge cases.** Edge cases refer to unusual, rare, or exceptional situations\n that aren't well represented in the training data. These cases can lead to\n limitations in the output of Gemini models, such as model\n overconfidence, misinterpretation of context, or inappropriate outputs.\n\n- **Model hallucinations, grounding, and factuality.** Gemini\n models might lack grounding and factuality in real-world knowledge, physical\n properties, or accurate understanding. This limitation can lead to model\n hallucinations, where Gemini for Google Cloud might\n generate outputs that are plausible-sounding but factually incorrect,\n irrelevant, inappropriate, or nonsensical. Hallucinations can also include\n fabricating links to web pages that don't exist and have never existed. For\n more information, see\n [Write better prompts for Gemini for Google Cloud](/gemini/docs/discover/write-prompts).\n\n- **Data quality and tuning.** The quality, accuracy, and bias of the prompt\n data that's entered into Gemini for Google Cloud\n products can have a significant impact on its performance. If users enter\n inaccurate or incorrect prompts, Gemini for Google Cloud\n might return suboptimal or false responses.\n\n- **Bias amplification.** Language models can inadvertently amplify existing\n biases in their training data, leading to outputs that might further reinforce\n societal prejudices and unequal treatment of certain groups.\n\n- **Language quality.** While Gemini for Google Cloud\n yields impressive multilingual capabilities on the benchmarks that we\n evaluated against, the majority of our benchmarks (including all of the\n fairness evaluations) are in American English.\n\n Language models might provide inconsistent service quality to different users.\n For example, text generation might not be as effective for some dialects or\n language varieties because they are underrepresented in the training data.\n Performance might be worse for non-English languages or English language\n varieties with less representation.\n- **Fairness benchmarks and subgroups.** Google Research's fairness analyses of\n Gemini models don't provide an exhaustive account of the various\n potential risks. For example, we focus on biases along gender, race,\n ethnicity, and religion axes, but perform the analysis only on the American\n English language data and model outputs.\n\n- **Limited domain expertise.** Gemini models have been trained\n on Google Cloud technology, but it might lack the depth of knowledge\n that's required to provide accurate and detailed responses on highly\n specialized or technical topics, leading to superficial or incorrect\n information.\n\n When you use the **Gemini** pane in the Google Cloud console,\n Gemini is not context aware of your specific environment, so\n it cannot answer questions such as \"When was the last time I created a VM?\"\n\n In some cases, Gemini for Google Cloud sends a specific\n segment of your context to the model to receive a context-specific\n response---for example, when you click the **Troubleshooting suggestions**\n button in the Error Reporting service page.\n\nGemini safety and toxicity filtering\n------------------------------------\n\nGemini for Google Cloud prompts and responses are checked\nagainst a comprehensive list of safety attributes as applicable for each use\ncase. These safety attributes aim to filter out content that violates our\n[Acceptable Use Policy](https://cloud.google.com/terms/aup). If an output is considered\nharmful, the response will be blocked.\n\nWhat's next\n-----------\n\n- Learn more about [how Gemini cites sources when helps you generate code](/gemini/docs/discover/works#how-when-gemini-cites-sources)."]]