A generative AI project typically follows these stages:
Stages of the AI Cycle:
AI Model Development Process
Identify Use Case:
Define the problem and decide the specific task the AI model will perform.
Narrow the scope early to save time and costs.
Experiment and Select:
Choose the data, model, and features.
Train and develop the model for your task.
Adapt, Align, and Augment:
Refine the model using techniques like prompt engineering and fine-tuning.
Improve performance based on feedback.
Evaluate:
Test the model using various metrics to ensure it meets your performance goals.
Deploy and Iterate:
Deploy the model into your infrastructure and integrate it with your application.
Optimize for performance and scalability.
Monitor:
After deployment, monitor the model for issues like hallucinations, poor reasoning, or incorrect information.
Key Considerations:
- Define Scope: The first step in any project is to clearly define the model’s scope and function.
- Training or Fine-Tuning: Decide whether to train your own model or fine-tune an existing one.
- Performance Improvement: Use prompt engineering or reinforcement learning from human feedback to align the model with human preferences.
- Iterate Frequently: Adjust the model continuously based on evaluation and feedback.
