---
title: 'Build With LLMs in 2027 | Learn Context Engineering, RAG & Multi-Agent AI Systems | Simplilearn'
source: 'https://youtube.com/watch?v=6C0xRzGeXxQ'
video_id: '6C0xRzGeXxQ'
date: 2026-10-23
duration_sec: 144
channel: 'Simplilearn'
---

# Build With LLMs in 2027 | Learn Context Engineering, RAG & Multi-Agent AI Systems | Simplilearn

> Source: [Build With LLMs in 2027 | Learn Context Engineering, RAG & Multi-Agent AI Systems | Simplilearn](https://youtube.com/watch?v=6C0xRzGeXxQ)

## Summary

This video is a promotional course overview from Simplilearn, presented by a Carnegie Mellon University instructor, introducing a comprehensive program on building reliable software systems with embedded large language models (LLMs). The course covers the science behind LLMs, context engineering, retrieval augmented generation (RAG), tool usage, and agentic system design, along with addressing challenges like hallucinations and bias. It is designed for software engineers, data scientists, and team leads seeking to master LLM integration.

### Key Points

- **Course Introduction and Instructor Credentials** [00:02] — The instructor teaches prompt engineering and context engineering at Carnegie Mellon University's requirements engineering lab, with a career dedicated to building reliable systems.
- **The Transformation of Software Engineering** [00:27] — Software engineering is being transformed by embedding LLMs into software and using software engineering agents, requiring engineers to get ahead of this shift.
- **Target Audience** [00:42] — The course is for data scientists, machine learning engineers, and team leads who are software engineers, or anyone wanting to build more reliable software with LLMs.
- **Course Curriculum Overview** [01:09] — The program covers how models are built, designed, and deployed, then moves into retrieval augmented generation (RAG), tool usage, and designing agentic systems.
- **Addressing LLM Limitations** [01:22] — The course discusses the rough edges of language models, including hallucinations, alignment, and building secure systems with embedded LLMs.
- **Deep Dive into Hallucination and Bias** [01:36] — The course goes deep into topics like hallucination and bias to help learners understand why responses may not align with goals and how to design systems with inherent bias in mind.
- **Course Structure and Capstone Project** [02:00] — The course requires 4-5 hours per week, including lectures and hands-on assignments, culminating in a capstone project where learners build a new system with an embedded LLM.
- **Learning Outcomes** [02:13] — By the end, learners will see LLM systems differently—not just as tools to experiment with, but as systems they can shape with real judgment.

### Conclusion

This course aims to equip professionals with the skills to build reliable, secure, and effective LLM-embedded systems, addressing both technical foundations and practical challenges. It emphasizes a shift from experimental use to deliberate, judgment-driven system design.

## Transcript

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
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
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
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
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
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
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
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]
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
>> [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
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.