Automate the Boring Stuff With Large Language Models

Learn how to use Large Language Models (LLMs) to get more done in less time. Increase efficiency by automating boring, redundant tasks! In this guided project, you will learn how to use LLMs to cover more ground with just a bit of Python code. As an industry use case example, we will take on the role of a manager at a call center who wants to review call transcripts and produce feedback for agents that have fallen below expectations. While this is one specific use case, the same idea may be extended to countless applications!

Guided Project

Artificial Intelligence

674 Enrolled

4.8

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At a Glance

Large Language Models (LLMs) have become incredibly powerful tools that are capable of many things, including text classification, summarization, and generation. Lo and behold, millions of work-hours are put into boring tasks that consist of these three things! In this course, we will have a look at how we can automate these boring tasks using LLMs at an incomparably lower cost!

A Look at the Project Ahead

In this project, we'll be learning the following:

The capabilities and general use cases of LLMs
How to interact with LLMs using the IBM Watson Python API
How to use LLMs to solve otherwise long and boring tasks
How to scale our solutions

What You'll Need

Introductory Python knowledge (or access to the Python documentation)

Offered By: IBMSkillsNetwork

Automate the Boring Stuff With Large Language Models

At a Glance

A Look at the Project Ahead

What You'll Need

Upskill with exclusive learning resources