In this module you will learn how to use Cortex LLM functions to accomplish many AI tasks, including: how to implement common generative AI use cases such as summarization, translation, sentiment analysis, and text classification with the Cortex Task-specific functions; how to implement other generative AI use cases using the Llama, Mistral, and Anthropic family of models with prompt engineering and the Cortex COMPLETE function; how to select an LLM for their use case, including when to select the larger or smaller models within a family of models; how to use the Cortex Helper functions to estimate token count and cost; and how to test their LLM calls for potential errors without incurring the associated inference cost.

In this module you will learn how to fine-tune an LLM to enable better performance for your use case. You will understand how to distill the capabilities of a large model into a smaller one. You will also learn: how Parameter Efficient Fine-Tuning (PEFT) can lower the training data requirements and reduce cost; how to generate training data and split it into training and evaluation datasets; how to fine-tune a foundation model, Mistral-7b, to learn to respond in a specific style using the Cortex FINETUNE function and the no-code Snowflake AI/ML Studio; testing your fine-tuned model using the Cortex COMPLETE function; how to build and share a simple AI application in Python for your fine-tuned model using Streamlit.