Generative AI Application Lifecycle
Capabilities and challenges of using generative AI
Capabilities of generative AI
Challenges of generative AI
Keep the capabilities and challenges in mind while navigating through the generative AI application lifecycle phases.
Generative AI application lifecycle
The generative AI application lifecycle refers to the process of using generative AI models within applications or systems.
2: Select a foundation model
2: Select a foundation model
Based on the identified requirements, an appropriate generative AI model is either selected from existing pre-trained models or developed from scratch. This decision depends on factors such as the availability of suitable pre-trained models, the complexity of the use case, and the availability of domain-specific data for training.
After deployment, user feedback, usage data, and performance metrics are continuously collected and analyzed to identify areas for improvement or new requirements. Based on this feedback, the generative AI model might be retrained, fine-tuned, or updated to enhance its performance and address any identified issues.
It's important to note that the generative AI application lifecycle is an iterative process, and different stages might have to be revisited or repeated as the application evolves, user needs change, or new advancements in generative AI technologies emerge.
Now let's explore the first phase of the generative AI application lifecycle, defining a use case.