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Part of the bootcamp
RAG Fundamentals for Everyone is one workshop inside AI Bootcamp for Software Engineers.
See the full path with mentor reviews and a portfolio outcome.
Go beyond copy-paste tutorials. Understand embeddings, vector databases, and chunking primitives, then build an agentic RAG pipeline that doesn't hallucinate in production.
Engineers are learning here from
Go beyond copy-paste tutorials. Learn how embeddings, vector stores, chunking, and retrieval actually work, then build a production-ready RAG pipeline with source attribution and guardrails.
Master the core building blocks of RAG, from embeddings to agentic retrieval.
What you'll ship
What you'll learn
Curriculum
Why RAG?
Understand why LLMs need external knowledge and how RAG solves the problem
Embeddings
Learn how text becomes numbers and build semantic search step by step
Vector stores
Store and search embeddings at scale using Qdrant vector database
Chunking strategies
Break documents into optimal pieces for embedding and retrieval
The RAG pipeline
Build a complete RAG system with LlamaIndex, source attribution, and guardrails
Agentic RAG
Add reasoning and tool use to your RAG system with the ReAct pattern
Who it's for
Your "chat with docs" feature breaks in production and you don't know why.
You understand pipelines but RAG retrieval and ranking is a black box.
You've used ChatGPT but want to build retrieval systems yourself.
FAQ
Yes, for running the Qdrant vector database locally. Setup instructions are included.
The first few lessons are free to preview. Full access requires Pro.
RAG pulls in context at query time. Fine-tuning changes the model itself. RAG is faster to build and easier to update, and that's what we focus on.
We use LlamaIndex for the final pipeline, but you'll understand each component first. You'll know what frameworks hide.
Master the core building blocks of RAG, from embeddings to agentic retrieval.