# The next challenge

<br />

The advent of large, generative models
introduces new challenges to implementing Responsible AI practices due to their
potentially open-ended output capabilities and many potential downstream uses. In addition to the AI Principles, Google has a [Generative AI Prohibited Use Policy](https://policies.google.com/terms/generative-ai/use-policy)
and [Generative AI Toolkit for Developers](https://ai.google.dev/responsible/docs).

Google also offers guidance about generative AI models on:

- [Safety](https://ai.google.dev/gemini-api/docs/safety-guidance)
- [Prompt Engineering](https://developers.google.com/machine-learning/resources/prompt-eng)
- [Adversarial Testing](https://developers.google.com/machine-learning/guides/adv-testing)

## Summary

Assessing AI technologies for fairness, accountability, safety, and privacy is
key to building AI responsibly. These checks should be incorporated into every
stage of the product lifecycle to ensure the development of safe, equitable, and
reliable products for all.

## Further learning

[Why we focus on AI -- Google AI](https://ai.google/why-ai/)

[Google Generative AI](https://ai.google/discover/generativeai/)

[PAIR Explorable: What Have Language Models Learned?](https://pair.withgoogle.com/explorables/fill-in-the-blank/)

[Responsible AI Toolkit \| TensorFlow](https://www.tensorflow.org/responsible_ai)