The development and deployment of generative AI applications require a systematic approach to ensure their relevance, reliability, security, and ethical alignment.
Generative AI Application Lifecycle (LLMOps)
The generative AI lifecycle is a framework for continuous development, deployment, and maintenance, representing a "Paradigm Shift from MLOps to LLMOps."
- Stages: Guides through "developing, deploying, and maintaining a generative AI application."
- Objectives: Helps "define your goals, measure your performance, identify your challenges, and implement your solutions."
- Continuous Improvement: Emphasizes the need to "monitor, evaluate, and improve it continuously" to ensure applications remain "relevant, reliable, and robust."
Designing for User Experience (UX) and Responsible AI
Building trust and transparency is paramount when designing AI applications.
- User Experience (UX): Focuses on "building trust and transparency in AI systems" and designing for "collaboration and feedback" to ensure user satisfaction.
- Responsible AI: Prioritizing Responsible AI principles is crucial to ensure outputs are "fair, non-harmful and more." This involves understanding core principles and implementing them through "strategy and tooling."
- Security: Securing generative AI applications involves addressing "common risks and threats to AI systems" and implementing "methods and considerations for securing AI systems." This includes planning "red teaming for large language models (LLMs) and their applications" and managing sensitive information.