CF

RAG for Real-World AI Applications

26m 55s
English
Paid
Updated September 2026

RAG for Real-World AI Applications is a 4-lesson 26 minutes self-paced course by Daniel Kelly, Garrison Snelling, Justin Schroeder, Vue School. Give AI Models Access to Your Own Data Language models only know what was in their training data — public docs, books, articles.

Course facts

Lessons
4
Duration
26 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Daniel Kelly, Garrison Snelling, Justin Schroeder, Vue School
Price
Premium

Give AI Models Access to Your Own Data

Language models only know what was in their training data — public docs, books, articles. Retrieval-Augmented Generation (RAG) is how you bridge that gap, letting a model work with information it was never trained on.

What You'll Cover

  • How embeddings represent text so it can be searched by meaning
  • How vector search retrieves the most relevant passages for a query
  • How that retrieved context gets assembled and passed to the model
  • Keeping your stored data current as it changes

The course keeps the code simple and practical, so you can see how RAG fits into a real application rather than just the theory behind it.

Additional

EARLY ACCESS. TO BE CONTINUE.

Who teaches RAG for Real-World AI Applications?

Daniel Kelly

Daniel Kelly thumbnail

Daniel Kelly is a US developer and Vue Mastery instructor, the co-author of Mastering Nuxt with Michael Thiessen, and one of the long-running independent voices on the Vue / Nuxt ecosystem. He is also a contributor to the modern AI-coding workflow content on CourseFlix's catalog.

His CourseFlix listing carries four Daniel Kelly courses spanning the AI-coding track: RAG for Real-World AI Applications, Mastering Reusable AI Workflows for Real-World Development, Turbo Mode — Optimizing Productivity with AI Tools and Agents, and The Future Proof Dev — An Intro to AI for Web Developers.

Material is paid and aimed at developers ready to make AI-coding tools and RAG patterns deliberate parts of their daily engineering practice. For broader content, see CourseFlix's AI-Assisted Coding, RAG, and AI Agents category pages.

Garrison Snelling

Garrison Snelling thumbnail

Garrison Snelling is a US developer and AI educator focused on the practical applied-AI workflow for working software engineers — RAG implementations, AI-assisted coding, agent orchestration, and the productivity workflows around modern AI tooling.

His CourseFlix listing carries four Garrison Snelling courses: RAG for Real-World AI Applications, Mastering Reusable AI Workflows for Real-World Development, Turbo Mode — Optimizing Productivity with AI Tools and Agents, and The Future Proof Dev — An Intro to AI for Web Developers.

Material is paid and aimed at developers ready to make AI-coding tools and agentic workflows core parts of their daily engineering practice rather than side experiments. For broader content, see CourseFlix's AI-Assisted Coding, AI Agents, and RAG category pages.

Justin Schroeder

Justin Schroeder thumbnail

Justin Schroeder is a US developer and the co-founder of FormKit — the popular Vue.js form-builder library — alongside his work as an independent AI-coding educator. He has been one of the more visible voices on the modern AI-assisted-engineering workflow.

His CourseFlix listing carries four Justin Schroeder courses on the AI-engineering track: RAG for Real-World AI Applications, Mastering Reusable AI Workflows for Real-World Development, Turbo Mode — Optimizing Productivity with AI Tools and Agents, and The Future Proof Dev — An Intro to AI for Web Developers.

Material is paid and aimed at developers ready to make AI-coding and RAG patterns deliberate parts of their daily practice. For broader content, see CourseFlix's AI-Assisted Coding, RAG, and AI Agents category pages.

Vue School

Vue School thumbnail

Vue School (vueschool.io) is a Greece-based Vue.js training platform founded by Alex Kyriakidis, an early Vue community member and one of the longest-running independent Vue educators. Vue School operates as both an on-demand course platform and a Vue / Nuxt consulting business, with course material that often emerges from real client engagements.

Course material covers the full Vue ecosystem: Vue 3 fundamentals through advanced Composition API patterns, Nuxt 3 production deployment, Pinia state management, Vue Router, the testing track with Vitest, TypeScript with Vue, real-time features with Pusher / WebSockets, and the broader full-stack Vue work. Vue School also publishes the popular Mastering Pinia course in collaboration with Pinia's author Eduardo San Martín Morote.

The CourseFlix listing under this source carries over 30 Vue School courses spanning that range. Material is paid; Vue School runs on per-course or membership pricing on the original platform. Courses are aimed at Vue developers from beginner through senior level building production Vue applications.

What lessons are included in RAG for Real-World AI Applications?

This is a demo lesson (10:00 remaining)

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#1: Intro to Real World RAG
All Course Lessons (4)
#Lesson TitleDurationAccess
1
Intro to Real World RAG Demo
03:13
2
Real World RAG Use-Cases from Top Companies
04:07
3
How RAG Works – A High Level Overview
05:58
4
Project Setup and Vibe Coding the Chat UI
13:37
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What courses are similar to RAG for Real-World AI Applications?

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Frequently asked questions

What prerequisites should I have before taking this course?
Before enrolling in this course, you should have a basic understanding of large language models and some experience with coding. Familiarity with concepts like embeddings and vector search will be beneficial, although the course will cover these topics in detail. No specific programming language expertise is required, but comfort with programming in general will help you follow along with the coding exercises.
What kind of projects will I build in this course?
In this course, you will work on building a system that can augment a language model with your own data using RAG. You will learn how to set up a project and code a chat UI, allowing you to create an AI system capable of answering questions based on your proprietary data. This practical approach ensures you gain hands-on experience in applying RAG to real-world projects.
Who is the target audience for this course?
The course is designed for developers and data scientists interested in enhancing AI applications with their own data. It's also suitable for professionals working in industries that require customized AI systems, such as finance, healthcare, or retail, where integrating unique datasets with language models is crucial.
How does the depth of this course compare to similar offerings?
This course provides a focused exploration of Retrieval Augmented Generation (RAG), concentrating on practical applications and integration into real-world projects. Unlike more general AI courses, this one zeroes in on using RAG to provide context and new information to language models, making it particularly valuable for those looking to apply AI to specific datasets.
What specific tools or platforms will I learn to use?
You will learn to use tools for embedding and vector search to augment language models with RAG. The course includes lessons on storing data, keeping it updated, and coding a chat UI, but it does not specify particular software platforms. Instead, it focuses on the underlying concepts and coding skills necessary to implement RAG solutions.
What topics or areas are not covered in this course?
This course does not cover the basics of machine learning or provide a deep dive into the internal workings of large language models. It assumes that you have prior knowledge in these areas. The focus is specifically on applying RAG to integrate external data into language models, rather than exploring the foundational principles of AI or machine learning.
How can the skills learned in this course benefit my career?
The skills gained from this course are valuable for careers in AI development and data science, particularly in roles that require integrating unique datasets with AI systems. Understanding how to use RAG to enhance language models with proprietary information can differentiate you in fields where customized AI solutions are in demand, such as technology, finance, and healthcare.