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Large language models (LLMs) can translate language, summarize text, generate
creative writing, generate code, power chatbots and virtual assistants, and
complement search engines and recommendation systems. At the same time, as an
early-stage technology, its evolving capabilities and uses create potential for
misapplication, misuse, and unintended or unforeseen consequences. Large
language models can generate output that you don't expect, including text that's
offensive, insensitive, or factually incorrect.
What's more, the incredible versatility of LLMs is also what makes it difficult
to predict exactly what kinds of unintended or unforeseen outputs they might
produce. Given these risks and complexities, Vertex AI generative AI APIs are designed with
Google's AI Principles in mind. However, it is important for developers to understand
and test their models to deploy safely and responsibly. To aid developers, the
Vertex AI Studio has built-in content filtering, and our generative AI APIs have
safety attribute scoring to help customers test Google's safety filters and
define confidence thresholds that are right for their use case and business.
Refer to the Safety filters and attributes
section to learn more.
When our generative APIs are integrated into your unique use case and context,
additional responsible AI considerations and
limitations
might need to be considered. We encourage customers to promote fairness,
interpretability, privacy and security
recommended practices.
Limitations you can encounter when using generative AI models include (but
are not limited to):
Edge cases: Edge cases refer to unusual, rare, or exceptional situations
that are not well-represented in the training data. These cases can lead to
limitations in the performance of the model, such as model overconfidence,
misinterpretation of context, or inappropriate outputs.
Model hallucinations, grounding, and factuality: Generative AI models
can lack factuality in real-world knowledge, physical properties, or
accurate understanding. This limitation can lead to model hallucinations,
which refer to instances where it can generate outputs that are
plausible-sounding but factually incorrect, irrelevant, inappropriate, or
nonsensical. To reduce this chance, you can ground the models to your
specific data. To learn more about grounding in Vertex AI, see
Grounding overview.
Data quality and tuning: The quality, accuracy, and bias of the prompt
or data input into a model can have a significant impact on its
performance. If users enter inaccurate or incorrect data or prompts, the
model can have suboptimal performance or false model outputs.
Bias amplification: Generative AI models can inadvertently amplify
existing biases in their training data, leading to outputs that can further
reinforce societal prejudices and unequal treatment of certain groups.
Language quality: While the models yield impressive multilingual
capabilities on the benchmarks we evaluated against, the majority of our
benchmarks (including all of fairness evaluations) are in the English
language. For more information, see the
Google Research blog.
Generative AI models can provide inconsistent service quality to
different users. For example, text generation might not be as effective
for some dialects or language varieties due to underrepresentation in
the training data. Performance can be worse for non-English languages or
English language varieties with less representation.
Fairness benchmarks and subgroups: Google Research's fairness analyses
of our generative AI models don't provide an exhaustive account of the
various potential risks. For example, we focus on biases along gender, race,
ethnicity and religion axes, but perform the analysis only on the English
language data and model outputs. For more information, see the
Google Research blog.
Limited domain expertise: Generative AI models can lack the depth of
knowledge required to provide accurate and detailed responses on highly
specialized or technical topics, leading to superficial or incorrect
information. For specialized, complex use cases, models should be tuned on
domain-specific data, and there must be meaningful human supervision in
contexts with the potential to materially impact individual rights.
Length and structure of inputs and outputs: Generative AI models have a
maximum input and output token limit. If the input or output exceeds this
limit, our safety classifiers are not applied, which could ultimately lead
to poor model performance. While our models are designed to handle a wide
range of text formats, their performance can be affected if the input data
has an unusual or complex structure.
Recommended practices
To utilize this technology safely and responsibly, it is also important to
consider other risks specific to your use case, users, and business context in
addition to built-in technical safeguards.
We recommend taking the following steps:
Assess your application's security risks.
Perform safety testing appropriate to your use case.
Configure safety filters if required.
Solicit user feedback and monitor content.
Report abuse
You can report suspected abuse of the Service or any generated output that
contains inappropriate material or inaccurate information by using the following
form:
Report suspected abuse on Google Cloud.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-29 UTC."],[],[],null,["# Responsible AI\n\nLarge language models (LLMs) can translate language, summarize text, generate\ncreative writing, generate code, power chatbots and virtual assistants, and\ncomplement search engines and recommendation systems. At the same time, as an\nearly-stage technology, its evolving capabilities and uses create potential for\nmisapplication, misuse, and unintended or unforeseen consequences. Large\nlanguage models can generate output that you don't expect, including text that's\noffensive, insensitive, or factually incorrect.\n\nWhat's more, the incredible versatility of LLMs is also what makes it difficult\nto predict exactly what kinds of unintended or unforeseen outputs they might\nproduce. Given these risks and complexities, Vertex AI generative AI APIs are designed with\n[Google's AI Principles](https://ai.google/principles/) in mind. However, it is important for developers to understand\nand test their models to deploy safely and responsibly. To aid developers, the\nVertex AI Studio has built-in content filtering, and our generative AI APIs have\nsafety attribute scoring to help customers test Google's safety filters and\ndefine confidence thresholds that are right for their use case and business.\nRefer to the [Safety filters and attributes](#safety_filters_and_attributes)\nsection to learn more.\n\nWhen our generative APIs are integrated into your unique use case and context,\nadditional responsible AI considerations and\n[limitations](#limitations)\nmight need to be considered. We encourage customers to promote fairness,\ninterpretability, privacy and security\n[recommended practices](https://ai.google/responsibilities/responsible-ai-practices/).\n\nSafety filters and attributes\n-----------------------------\n\nTo learn how to use safety filters and attributes for an API,\nsee [Gemini API in Vertex AI](/vertex-ai/generative-ai/docs/multimodal/configure-safety-attributes).\n\nModel limitations\n-----------------\n\n*Limitations you can encounter when using generative AI models include (but\nare not limited to):*\n\n- **Edge cases**: Edge cases refer to unusual, rare, or exceptional situations\n that are not well-represented in the training data. These cases can lead to\n limitations in the performance of the model, such as model overconfidence,\n misinterpretation of context, or inappropriate outputs.\n\n- **Model hallucinations, grounding, and factuality** : Generative AI models\n can lack factuality in real-world knowledge, physical properties, or\n accurate understanding. This limitation can lead to model hallucinations,\n which refer to instances where it can generate outputs that are\n plausible-sounding but factually incorrect, irrelevant, inappropriate, or\n nonsensical. To reduce this chance, you can ground the models to your\n specific data. To learn more about grounding in Vertex AI, see\n [Grounding overview](/vertex-ai/generative-ai/docs/grounding/overview).\n\n- **Data quality and tuning**: The quality, accuracy, and bias of the prompt\n or data input into a model can have a significant impact on its\n performance. If users enter inaccurate or incorrect data or prompts, the\n model can have suboptimal performance or false model outputs.\n\n- **Bias amplification**: Generative AI models can inadvertently amplify\n existing biases in their training data, leading to outputs that can further\n reinforce societal prejudices and unequal treatment of certain groups.\n\n- **Language quality** : While the models yield impressive multilingual\n capabilities on the benchmarks we evaluated against, the majority of our\n benchmarks (including all of fairness evaluations) are in the English\n language. For more information, see the\n [Google Research blog](https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html).\n\n - Generative AI models can provide inconsistent service quality to different users. For example, text generation might not be as effective for some dialects or language varieties due to underrepresentation in the training data. Performance can be worse for non-English languages or English language varieties with less representation.\n- **Fairness benchmarks and subgroups** : Google Research's fairness analyses\n of our generative AI models don't provide an exhaustive account of the\n various potential risks. For example, we focus on biases along gender, race,\n ethnicity and religion axes, but perform the analysis only on the English\n language data and model outputs. For more information, see the\n [Google Research blog](https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html).\n\n- **Limited domain expertise**: Generative AI models can lack the depth of\n knowledge required to provide accurate and detailed responses on highly\n specialized or technical topics, leading to superficial or incorrect\n information. For specialized, complex use cases, models should be tuned on\n domain-specific data, and there must be meaningful human supervision in\n contexts with the potential to materially impact individual rights.\n\n- **Length and structure of inputs and outputs**: Generative AI models have a\n maximum input and output token limit. If the input or output exceeds this\n limit, our safety classifiers are not applied, which could ultimately lead\n to poor model performance. While our models are designed to handle a wide\n range of text formats, their performance can be affected if the input data\n has an unusual or complex structure.\n\nRecommended practices\n---------------------\n\nTo utilize this technology safely and responsibly, it is also important to\nconsider other risks specific to your use case, users, and business context in\naddition to built-in technical safeguards.\n\nWe recommend taking the following steps:\n\n1. Assess your application's security risks.\n2. Perform safety testing appropriate to your use case.\n3. Configure safety filters if required.\n4. Solicit user feedback and monitor content.\n\nReport abuse\n------------\n\nYou can report suspected abuse of the Service or any generated output that\ncontains inappropriate material or inaccurate information by using the following\nform:\n[Report suspected abuse on Google Cloud](https://support.google.com/code/contact/cloud_platform_report).\n\nAdditional resources\n--------------------\n\n- Learn about [abuse monitoring](/vertex-ai/generative-ai/docs/learn/abuse-monitoring).\n- Learn more about Google's recommendations for [Responsible AI practices](https://ai.google/responsibilities/responsible-ai-practices/?category=general).\n- Read our blog, [A shared agenda for responsible AI progress](https://blog.google/technology/ai/a-shared-agenda-for-responsible-ai-progress/)"]]