Understand RAG architecture
Pipelines, vector databases, chunks, embeddings and cosine similarity — how retrieval actually works.
What you'll build
Real skills, real projects — not another slide deck.
Ground AI in real documents
Make an assistant answer from your knowledge base instead of hallucinating.
Build with Dify
Assemble a working RAG application with Dify and a vector store, end to end.
Know memory vs RAG
Choose the right approach — conversational memory, retrieval, or both — for each use case.
- A vector-database-backed retrieval pipeline from scratch
- A working document Q&A assistant on real documents
- A knowledge base with proper chunking and embeddings
- A RAG-enabled business assistant that retrieves and responds
- An enterprise-style knowledge system as a portfolio project
Who it's for
The technique behind enterprise AI assistants
RAG — Retrieval-Augmented Generation — is how serious AI assistants answer accurately from company data instead of making things up. It's one of the most sought-after AI skills right now, because almost every business wants an assistant that knows its own documents, policies and products. This course teaches the architecture and has you build a working system with Dify and a vector database.
It suits developers, AI builders, founders and professionals who want to create real knowledge assistants. ONROL is project-first, so you finish with a document Q&A assistant and an enterprise-style knowledge system in a portfolio you keep.
The ONROL method
Build the evidence. Keep the proof.
ONROL is an execution school, not a lecture hall. Every session you ship something real — an automation, an agent, an app — and you keep it. You don't leave with a certificate alone; you leave with a portfolio of working AI products that proves what you can do.
Questions
Straight answers.
What is a RAG course and why does it matter?
RAG (Retrieval-Augmented Generation) lets AI answer from your own documents accurately. It's one of the most in-demand AI skills because nearly every business wants an assistant grounded in its own data — and this course has you build one.
Do I need to be a heavy coder?
No. You build RAG systems with Dify and vector databases using a low-code approach, so builders and technically-minded professionals can create real retrieval systems without deep programming.
What will I build?
A working document Q&A assistant, a properly chunked knowledge base, and an enterprise-style knowledge system — real, portfolio-ready projects that demonstrate a genuinely in-demand skill.
How does this relate to AI agents?
RAG is often a component inside agents — it gives them accurate knowledge. This course focuses on retrieval; our AI Agents course builds the agent workflows that can use it.
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