About generative AI

Generative AI is a type of artificial intelligence that focuses on generating content, typically in the form of text, images, or other media. This content appears human-generated, making it useful in tasks like natural language understanding, text generation, image synthesis, and more. Generative AI is commonly applied in tasks such as text completion, language translation, content generation, and creative applications like art generation. At AWS we are committed to developing generative AI responsibly, taking a people-centric approach that prioritizes education, science, and our customers, to integrate responsible AI across the end-to-end AI lifecycle. Learn more  about core dimensions of responsible generative AI, and refer to AWS AI documentation  to learn more key AI concepts and terms. As generative AI rapidly evolves, Cloudscape is prioritizing the delivery of those building blocks that support the most common emerging use cases. In a space where experimentation is key, our goal is to deliver fast, learn, and iteratively develop the patterns and components that teams need.

To unblock teams and speed up innovation, we provide generative AI design resources as part of Cloudscape’s Figma library stack. See generative AI patterns for additional context.

Responsible AI at AWS  relies on the following core dimensions: fairness, explainability, privacy and security, robustness, governance and transparency. Building upon these dimensions, use the following principles to design and develop generative AI experiences in AWS.

Evaluate the use cases and their complexity
Generative AI can be useful for quickly creating content like images, text, audio etc. Before employing generative AI, evaluate if the use case would benefit from automating generation of custom outputs. Prioritize automation of tasks requiring basic text or image generation. Expand on generative AI adoption to support more complex use cases gradually.

Focus on reliability and usefulness
Ensure generative AI meets high accuracy, quality and safety standards before deployment. Prioritize generating outputs that are directly useful for the user's goals and context.

Maintain security and user privacy
Maintain security by using systems to detect and filter harmful or abusive content generated by AI. Don’t collect or use customer data to improve services without user consent.

Give users control
Enable users to control how generative AI is employed in a task. Allow users to guide the experience, set parameters, and override AI if needed.

Be transparent and communicate errors
Be transparent about capabilities and limitations of generative AI. Make it clear when content is AI-generated to earn trust with users. When errors occur or confidence in the output generated by AI is low, communicate it clearly to the user to set expectations.

Mitigate biases and enable feedback
Take steps to mitigate potential biases in training data. Continue testing for unfair outputs and enable user feedback to continually train the AI to improve quality. Expose settings to encourage diverse, inclusive output from the AI when possible.

If you want to go fast, go alone. If you want to go far, go together. See how to connect with the Cloudscape team through from our Connect page.