[00:02] at Carnegie Mellon University. >> [music] requirements engineering lab. For the past few years, I've been teaching courses on prompt [music] engineering and context engineering and my dedicated
[00:15] my whole career to trying to build more reliable systems. That's why I'm trying to bring all of this information together in [music] this course for you. The reason we need this course now is because software engineering is being
[00:27] transformed in so many ways. The way we used to build software, we're not doing software engineering agents, we're embedding LLMs into the software, and so this course is really focused on how to get ahead on that transformation and how
[00:42] to enable engineers to be able to build systems with LLMs embedded within them. data scientists, machine learning engineers, and team leads who are software [music] with LLMs embedded within them. It's really for anybody
[00:57] who's trying to get a handle on this groundbreaking technology to figure out how to build more reliable software. This program is built on the foundations of science for large language models. It begins with how models are built and
[01:09] designed and deployed and it moves into topics like retrieval augmented generation, tool usage, and it pulls all those topics together [music] into how to design agentic systems. In addition to that, we talk about the rough edges
[01:22] of language models including topics like hallucinations, alignment, and how to build [music] systems that are secure when you have a language model embedded. course [music] has to do with topics like hallucination and bias. These are
[01:36] topics that are often sitting on the edges. When you're prompting a language >> you don't understand exactly why the response you're getting doesn't align with your goals and objectives. We're going to go deep into that topic [music]
[01:48] in order to understand how to really design systems where they have this bias inherent within them. This course is about 4 to 5 hours a week which includes both lectures as well as hands-on assignments that all come together in a
[02:00] >> [music] >> In that capstone project, you're going to build a new system with an embedded LLM. At the end of this course, you'll walk away seeing these systems very differently. Not just as tools you
[02:13] experiment with, [music] but as ones you can shape with real judgment. I hope can shape with real judgment. I hope you'll join us.