Course Introduction

1. Course Introduction

Now that we have gotten our first taste of how we can use generative AI in Snowflake, let's take a closer look at using Cortex LLM functions. As you might remember from before, we used the Snowflake Cortex complete function to prompt the foundation models with instructions to summarize the transcript in JSON format. In this module, we will look at what LLM functions are and how we can use them to accomplish complex natural language tasks. First, we will explore how to use complete with custom prompts to run a natural language task. Next, we will learn more about the out-of-the-box task-specific functions. These will include translation, sentiment analysis, summarization, and text classification. We will also look at how to use two of the helper functions that come with Cortex, count tokens and try complete. And then we will learn about the role-based access controls or RBAC you need to use these functions. And lastly, we will build a simple streamlined application where we will analyze call transcripts using Snowflake Cortex. We will use Cortex LLM functions and Streamlit to bring this all together. Let's get into it.

2. Let's practice!

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