Generative AI

Q: What are the individual stages and decisions involved in developing Generative AI applications?

A: There are a number of stages and decisions involved in developing Generative AI applications. Here are some of the most important ones:

  1. Choosing a foundation model. One of the first decisions you need to make is whether to use a foundation model that is already available or to pre-train your own model. Foundation models are large language models that have been trained on a massive dataset of text and code. They can be used as a starting point for developing your own Generative AI application.

  2. Fine-tuning and customizing the model. If you choose to use a foundation model, you will need to fine-tune and customize it for your specific application. This involves training the model on your own dataset of text and code. The amount of fine-tuning and customization that you need to do will depend on the specific requirements of your application.

  3. Choosing the right model sizing. The size of the model that you choose will affect the performance and accuracy of your application. Larger models are more accurate, but they also require more computing resources. You need to choose a model size that is appropriate for your application's needs.

Q: What are some factors to consider when evaluating foundation models?

A: There are a number of factors to consider when evaluating foundation models, including:

  • The size of the model.

  • The dataset that the model was trained on.

  • The performance of the model on a variety of tasks.

  • The availability of documentation and support for the model.

Q: How do I choose the right model sizing for my application?

A: The right model sizing for your application will depend on a number of factors, including:

  • The complexity of your application.

  • The amount of data that you have.

  • The computing resources that you have available.

In general, larger models are more accurate, but they also require more computing resources. If you have a complex application with a lot of data, you will need to use a larger model. However, if you have limited computing resources, you may need to use a smaller model.