Factors to Consider When Selecting a Generative AI Model

Start by defining the task, such as text generation, image creation, or code generation, because models are optimized for different tasks. Then weigh these factors:

• Model type: which model family fits the task (see the next lesson).
• Performance requirements: accuracy and reliability of output. Test against different datasets and keep monitoring over time.
• Capabilities: what the model does well, such as text, images, or multimodal tasks, and how much control or customization it allows.
• Constraints:
  • Computational resources (GPU, CPU, memory)
  • Data availability (size and quality of training data)
  • Deployment requirements (on premises or cloud)
• Compliance: bias, privacy, misuse, fairness, transparency, accountability, hallucination, and toxicity, especially in healthcare, finance, and legal. The model must follow regulations and ethical guidelines.
• Cost: larger models are usually more accurate but cost more and have fewer deployment options. Smaller models are cheaper, faster, and easier to deploy. Count deployment, maintenance, hardware, and software costs too.