How to Choose the Right Generative AI Foundation Model: A Practical Guide

If you have a use case for generative AI, selecting the best foundation model can feel overwhelming. With the wide array of models out there, it’s tough to pinpoint the right one.

Different models vary in terms of training data, parameter counts, and deployment methods, and choosing the wrong one could lead to unwanted outcomes like biased outputs or hallucinations—where the model produces information that is simply incorrect.

You might be tempted to just grab the largest, most powerful model out there to cover all your tasks.

However, larger models come with heavy costs—both in terms of computational resources and potential complications. So, often the smarter strategy is to choose the model that best fits the size and scope of your specific use case.

In this guide, I’ll walk you through a simple but effective framework for selecting the ideal AI model. By following these six stages, you can make informed decisions and avoid potential pitfalls. Let’s dive into each stage and see how it works.

How to Choose the Right Generative AI Foundation Model

Stage 1: Clearly Define Your Use Case

The very first step is to precisely articulate your use case. What exactly do you want generative AI to do for you? Are you using it for content generation, customer support, or some other specific function? By clearly defining your needs, you’ll have a solid foundation for selecting the right model.

For example, let’s say your use case is text generation. You want AI to write personalized emails for your marketing campaign. That’s your starting point—once you have that nailed down, everything else falls into place.

Stage 2: List Available Foundation Models

After defining your use case, list the foundation models available to you. In many cases, you’ll already have access to a subset of models through your organization’s existing infrastructure. Identify which ones are suitable for your task.

In our text generation example, let’s assume that your organization already uses two foundation models: LLaMA 2, a model from Meta with 70 billion parameters, and Granite, a smaller general-purpose model from IBM with 13 billion parameters. Both are options you can evaluate for your needs.

Stage 3: Evaluate Model Size, Performance, and Risks

Once you’ve shortlisted the models, the next step is to analyze each one’s characteristics. Start by checking each model’s size, performance, and potential risks.

A good resource to use here is the model card, which offers detailed information about the training data and any specific fine-tuning done for particular use cases, like text generation, sentiment analysis, or document summarization.

For instance, if a model has been pre-trained on data similar to your use case, it could be more efficient at processing prompts with little additional input, thanks to zero-shot prompting.

Zero-shot prompting allows the model to perform tasks without needing multiple examples to learn from—just a single well-written prompt can suffice.

Stage 4: Assess Performance Factors: Accuracy, Reliability, and Speed

Next, you need to evaluate the performance of each model based on three key factors:

1. Accuracy – How close is the output to what you want? You can measure accuracy by using relevant metrics that apply to your use case. For text generation, metrics like BLEU (BiLingual Evaluation Understudy) are often used to evaluate the quality of outputs in translation tasks.
2. Reliability – Does the model consistently deliver trustworthy results? Reliability is built on consistency, explainability, and a lack of unwanted outcomes like hate speech or toxicity. Trust is crucial, and it comes from the transparency of the model’s training data and its ability to produce reliable results.
3. Speed – How fast can the model generate responses? This is especially important for real-time applications. Larger models may be more accurate but slower, while smaller models are typically faster. The trick is finding the right balance between speed and accuracy for your use case.

Stage 5: Test the Models

Now it’s time to put the models to the test. Select the model that you believe will provide the best results and run it against your specific prompts. Testing is the only way to truly understand how well a model will perform for your unique use case.

For example, if you’re focusing on text generation for personalized emails, test both LLaMA 2 and Granite using sample prompts from your marketing campaign. Compare the results using the performance metrics you identified earlier. Which model produces the most accurate, reliable, and timely output?

Stage 6: Choose the Model that Provides the Most Value

After running your tests, the final step is to make an informed decision. Choose the model that offers the best trade-off between performance, speed, and cost.

For example, LLaMA 2 may be more accurate, but Granite could be faster and more cost-effective. Depending on your budget, deployment environment, and overall goals, you’ll need to decide which model provides the most value for your specific application.

Deployment Considerations: Cloud vs. On-Premise

One critical aspect of your decision will be where and how to deploy the model. For instance, if you choose LLaMA 2, an open-source model, you can run it on a public cloud or deploy it on-premise if you need more control over your data and security.

Deploying on-premise offers greater control and security benefits but comes at a higher cost in terms of computing power, especially when running large models that require multiple GPUs.

Public clouds offer more flexibility, but you have less control over the model. The decision to use public cloud or on-premise largely depends on your organization’s needs, budget, and security requirements.

Multi-Model Approach: Tailoring AI Models to Specific Use Cases

It’s also worth considering that most organizations have multiple use cases, and different foundation models may be better suited to different tasks. This is where a multi-model approach comes in—using different AI models for different tasks to get the best results.

By applying this framework, you can ensure that each of your use cases is matched to the most suitable model, maximizing the value of your generative AI applications.

Final Thoughts

In conclusion, choosing the right foundation model for generative AI is no simple task, but with a structured approach, it becomes manageable. By defining your use case, evaluating available models, and carefully testing them, you can make informed decisions that avoid costly mistakes.

Whether you’re generating personalized emails, conducting sentiment analysis, or building complex AI-driven applications, this model selection framework can guide you toward the most effective solution.

Keep in mind the balance between performance, speed, and cost, and don’t be afraid to take a multi-model approach if it’s the best fit for your organization.

With the right model in place, your AI-driven project will be well on its way to success!