Factors to Consider When Selecting a Generative AI Model
When selecting a generative AI model, there are several important factors to consider. First, it's essential to define the specific task or application you want the model to perform, such as text generation, image creation, or code generation. Models are optimized for different tasks, so choosing the right one is crucial for achieving the desired results.
Some of the key factors to consider when selecting an appropriate generative AI model include the following:
- Model types
- Performance requirements
- Capabilities
- Constraints
- Compliance
Models
There are many model types. The following list is a non-exhaustive list of model options. It shows the model names, the tasks those models can do, and some sample use cases of how the model has been used to solve business problems. Each model has its own capabilities and challenges.
AI21 labs
Jurassic-2 Models
Tasks
- Text generation
- Summarization
- Paraphrasing
- Chat
- Information extraction
Use Cases
- Financial services – summarize lengthy documents
- Retail – generate product descriptions
Amazon
Amazon Titan
Tasks
- Text summarization
- Classification
- Open-ended Q&A
- Information extraction
- Embeddings
- Search
Use Cases
- Advertising – create studio quality images
- Customer service – generate real-time abstract summaries
Anthropic
Claude
Tasks
- Content generation
- Text translation
- Question answering
- Text summarization
- Code explanation and generation
Use Cases
- Developer – code generation and debugging
- Legal – parse legal documents and answer questions
Stability AI
Stable Diffusion
Tasks
- Generate photo realistic images from text input
- Improve quality of generated images
Use Cases
- Gaming and metaverse – create characters, scenes, and worlds
- Advertising and marketing – create ad campaigns and marketing assets
Cohere
Command
Tasks
- Text generation
- Information extraction
- Question and answering
- Summarization
Use Cases
- Customer service – support chatbots
- Retail – provide product descriptions
- Healthcare – summarize key ideas from long text
Meta
Llama
Tasks
- Question answering
- Chat
- Summarization
- Paraphrasing
- Sentiment analysis
- Text generation
Use Cases
Customer service support – chatbots
For additional information about these models and other models, review the websites below:
Amazon Bedrock
To learn about this service, the FMs, and more, choose the following.
Go to website
What are foundation models?
To learn about FMs, choose the following.
Go to website
Performance requirements
Performance requirements are another factor to consider when selecting a generative AI model. These requirements include accuracy, reliability of the output, and others. Assess the overall performance of the model to evaluate its suitability for a particular task. You should also test the model against different datasets to ensure reliability. Finally, monitor its performance over time to ensure it remains consistent.
Constraints
Consider the constraints of a model such as the following:
- Computational resources (for example, available GPU power, CPU power, or memory)
- Data availability (for example, size and quality of training data)
- Deployment requirement (for example, on premises or cloud)
Some models might have higher resource demands or require specific hardware configurations, which could impact their use case.
Capabilities
Another factor to consider is the model's capabilities. Generative AI encompasses a wide range of capabilities. It can perform different tasks with varying degrees of output quality and levels of control or customization. For instance, some models might be better at generating text, whereas others might excel at generating images or performing multimodal tasks such as text-to-image generation. Therefore, it is important to understand the specific capabilities required for your application before selecting a generative AI model.
Compliance
Compliance is another factor. Generative AI models can pose moral concerns, including biases, privacy issues, and potential misuse. When evaluating a particular model, consider its compliance and moral implications, particularly in sensitive domains like healthcare, finance, and legal applications. One should consider factors such fairness, transparency or traceability, accountability, hallucination, and toxicity. Additionally, the model should adhere to relevant regulations and ethical guidelines.
Cost
Another key factor is cost. Generative AI models can vary in terms of cost. Consider the trade-off between the size and the speed of the model. Larger models are usually more precise, but they are expensive and offer few deployment options. Conversely, smaller models are cheaper and faster, and they offer more deployment alternatives.
By using generative AI for content creation, you can reduce labor costs and increase efficiency, especially for repetitive tasks that require significant human effort.
Remember to evaluate all expenses related to deployment, maintenance, hardware, software, and other associated costs.
By considering these factors, you can select a generative AI model that best fits your specific needs.
| Now that you understand the factors to consider when selecting a model, let's discuss some key business metrics associated with AI models. |
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