What are the major steps in the lifecycle of a Generative AI project?

The major steps in the lifecycle of a Generative AI project typically follow a structured approach that ensures the project is well-defined, developed, evaluated, and maintained. The correct sequence being highlighted involves scoping the project first, which allows the team to understand the objectives, requirements, constraints, and resources needed. This foundational step is crucial as it sets the direction for the project's development.

Following the scoping phase, the project moves on to building or improving the system. This is the stage where the actual development occurs, including model training, data processing, and system integration. The iterative nature of AI projects often means that there will be ongoing improvements based on insights gained during development.

After the system has been built, internal evaluation comes into play. This assessment is vital to ensure that the system functions as intended and meets the predetermined criteria established during the scoping phase. Internal evaluation helps identify any areas for improvement or adjustments needed before the project is deployed in a real-world environment.

The final step is to deploy and monitor the system. This involves implementing the AI solution and continuously observing its performance and impact in real-time scenarios. Ongoing monitoring is necessary to ensure that the system maintains its effectiveness and can adapt to any changes or challenges it may encounter in deployment.