Generative AI Application Lifecycle

The lifecycle describes how generative AI models are built into applications. It's iterative: stages get revisited as needs change or better models appear.

1. Define a use case: the problem, the requirements, and stakeholder expectations.
2. Select a foundation model: use a pre-trained model or build one from scratch, depending on whether a suitable model exists, how complex the use case is, and how much domain data you have.
3. Improve performance: prompt engineering, RAG, fine-tuning, and agents.
4. Evaluate: human evaluation, benchmark datasets, and automated metrics.
5. Deploy: integrate the model into the target environment.
6. Monitor and improve: collect feedback, usage data, and metrics, then retrain, fine-tune, or update the model.