Selecting the right foundation model for a generative AI use case is a critical decision. With a vast number of models available—each with different training data, parameter counts, and capabilities—choosing the wrong model can lead to unintended consequences, such as biases from training data, hallucinated outputs, or excessive computational costs. Instead of defaulting to the largest and most complex models, it's often more effective to select the right-sized model for the specific use case.

AI Model Selection Framework

To streamline the decision-making process, an AI model selection framework can help evaluate and compare different foundation models. This framework consists of six stages:

1. Clearly Define Your Use Case

The first step is to precisely articulate what you need the generative AI model to do. Different tasks require different models—some models excel in text generation, while others are better suited for image creation, summarization, or sentiment analysis. For example, if your goal is to generate personalized marketing emails, your choice of models will focus on text generation capabilities.

2. Identify Available Models

Once the use case is defined, create a shortlist of foundation models that could be suitable. If your organization already uses specific models, evaluate those first. For instance, if an enterprise is running Llama 2 (70B parameters, by Meta) and Granite (13B parameters, by IBM) for other tasks, those models can be evaluated for the new use case before exploring additional options.

3. Evaluate Model Characteristics

Each shortlisted model should be assessed based on key characteristics:

• Size: The number of parameters in a model directly affects its computational needs. Larger models tend to be more powerful but also more expensive to run.
• Performance: Some models are fine-tuned for specific tasks (e.g., text summarization vs. text generation), and their effectiveness can vary.
• Cost: Computational cost (both in terms of hardware requirements and cloud pricing) is a major factor.
• Risks: Consider potential biases, ethical concerns, and the likelihood of producing hallucinated outputs.
• Deployment Options: Whether the model can run on cloud, on-premise, or edge systems.

A useful tool for this step is the model card, which provides insights into how a model was trained, what data it was exposed to, and any known biases or limitations.

4. Compare Performance for Your Use Case

Model performance can be evaluated based on three critical factors:

1. Accuracy: How well does the generated output align with the desired results? Accuracy can be objectively measured using benchmarks relevant to the use case. For example, BLEU (Bilingual Evaluation Understudy) is a common metric for evaluating text translation models.
2. Reliability: This includes consistency, explainability, and trustworthiness of the model. Some models introduce bias or generate toxic content, making reliability a crucial factor. Transparent documentation of training data is also key.
3. Speed: How quickly does the model generate responses? There’s often a trade-off between speed and accuracy—larger models may provide better results but at the cost of slower response times.