With the downstream commercialization of large language models (LLMs), businesses now look forward to using these powerful language models like PaLM on their private enterprise data. But as powerful as these models are in their raw form, they need help understanding data they are not trained on. This talk will cover how these models can be effectively leveraged to create private AI assistants for businesses.

This talk will start by first focusing on the concept of foundation models (like PALM & PALM-2) and their usefulness.

Then we will talk about how although finetuning the model by retraining it on custom data seems like a potential solution, it can be costly and data intensive.

We will then talk about modern zero-shot, few-shot, and prompt-free finetuning strategies that are more effective with fewer data and can be way cheaper and faster to execute.

The talk will then move towards how the Vertex AI platform can be used to finetune and run inference on powerful LLMs like PALM.

Finally, as the leaving message, the talk will highlight the benefits of using LLMs on private data and how Vertex AI can streamline and simplify the entire process of adopting LLMs in the enterprise