Developing Generative Artificial Intelligence Solutions

In this course, you will explore the generative artificial intelligence (generative AI) application lifecycle, which includes the following:

• Defining a business use case
• Selecting a foundation model (FM)
• Improving the performance of an FM
• Evaluating the performance of an FM
• Deployment and its impact on business objectives

This course is a primer to generative AI courses, which dive deeper into concepts related to customizing an FM using prompt engineering, Retrieval Augmented Generation (RAG), and fine-tuning.

This course is intended for the following:

• Individuals interested in machine learning and artificial intelligence, independent of a specific job role

Developing Generative AI Solutions is part of a series that facilitates a foundation on artificial intelligence, machine learning, and generative AI. If you have not done so already, it is recommended that you complete these two courses:

• Fundamentals of Machine Learning and Artificial Intelligence
• Exploring Artificial Intelligence Use Cases and Applications

In this course, you will learn how to do the following:

• Identify selection criteria to choose pre-trained models.
• Define Retrieval Augmented Generation (RAG) and describe its business application.
• Explain the cost trade-offs of various approaches to foundation model customization.
• Understand the role of agents in multi-step tasks.
• Understand approaches to evaluate foundation model performance.
• Identify relevant metrics to assess foundation model performance.