The generative AI lifecycle provides a structured framework for developing and deploying AI solutions. It consists of five key stages: defining a use case, selecting a foundation model, improving performance, evaluating results, and deploying the application.
This iterative process begins with clearly articulating the business problem and requirements, then choosing an appropriate pre-trained model as a starting point.
Throughout the lifecycle, there's a focus on continuous refinement to ensure the AI solution remains effective and aligned with business objectives.
While generative AI has numerous applications, it's equally important to recognize situations where it might not be the most appropriate solution. These include situations with high accuracy and reliability requirements, ill-defined or constantly changing problems, insufficient data quality, the need for explainability and transparency, cost-benefit considerations, and ethical concerns.
Sometimes, other methods work better than AI. This includes simple tasks that are solvable with rule-based solutions, or when model costs outweigh business benefits.
AI Use Cases
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