Who is this course for?

This course suits professionals and practitioners who want hands-on skills with Basic to Advanced: Retreival-Augmented Generation (RAG). Some familiarity with AI tools is helpful but not required.

Is the content up to date for 2025/2026?

Yes. The curriculum has been updated to reflect the latest tools, APIs, and best practices as of 2025-2026.

Do I need to know machine learning?

No, you don't need ML expertise. The course focuses on using RAG frameworks and APIs. Basic Python knowledge is sufficient to follow along.

What vector databases are covered?

The course covers multiple vector databases including Pinecone, Weaviate, and Chroma. You'll learn when to use each one based on your use case.

Can I build RAG systems for my company?

Absolutely. The course teaches production-ready patterns used in enterprise RAG applications. You'll learn document chunking, embedding strategies, and deployment best practices.

How long does it take to complete?

The video content is 2.5 hours across 18 lectures. With hands-on practice building your own RAG systems, most students complete in 1-2 weeks.

Will I get code examples?

Yes, all code repositories are included. You'll get complete working examples for each RAG pattern covered in the course.

All Levels · Video course★ 4.4 · 4,147 students

Basic to Advanced: Retreival-Augmented Generation (RAG)

Master RAG from fundamentals to production with hands-on vector store and LLM integration projects

Video-first training aligned with our skills registry and MCP directory.

Enroll on Udemy All courses

18 lectures2.5 hours on-demand videoLifetime access

What you'll learn

Build production-ready RAG systems from scratch
Implement vector databases for semantic search
Design multi-modal retrieval pipelines
Integrate LLMs with custom knowledge bases
Deploy RAG applications for real-world use cases
Optimize chunking strategies and embedding models

Who it's for

AI Engineers

Developers building AI applications who need to implement retrieval-augmented generation for better LLM outputs

Data Scientists

ML practitioners exploring RAG pipelines, vector databases, and semantic search for their projects

Backend Developers

Engineers adding AI search and Q&A capabilities to existing applications using RAG patterns

Course curriculum

18 lectures · 2.5 hours total

1. What is RAG and why it matters8 min
2. Vector embeddings explained10 min
3. RAG vs fine-tuning comparison7 min

What you'll be able to build after this course

By the end of the program, you'll be able to:

Build production-ready RAG systems from scratch
Implement vector databases for semantic search
Design multi-modal retrieval pipelines
Integrate LLMs with custom knowledge bases
Deploy RAG applications for real-world use cases

Popular projects students build:

→Document Q&A chatbots
→Knowledge base search engines
→Content recommendation systems
→Multi-document research assistants
→Enterprise RAG pipelines

Hands-on projects

Real-world projects you'll build during the course