Build RAG That Actually Works in Production
Go beyond copy-paste tutorials. Understand embeddings, vector databases, and chunking primitives, then build an agentic RAG pipeline that doesn't hallucinate in production.
What You Will Learn
- ✓Understand every piece of a RAG pipeline — embeddings, vector stores, retrieval
- ✓Know what breaks in production RAG and how to fix it
- ✓Choose chunking strategies that actually give accurate answers
- ✓Ship a "chat with your docs" system with proper source citations
- ✓Extend your pipeline with an agent that finds the best answer automatically
Who Is This For
- Developers adding AI to products — Your "chat with docs" feature breaks in production and you don't know why.
- Data engineers — You understand pipelines but RAG retrieval and ranking is a black box.
- AI-curious developers — You've used ChatGPT but want to build retrieval systems yourself.
Curriculum
Why RAG?
3 lessons
Embeddings
3 lessons
Vector Stores
3 lessons
Chunking Strategies
3 lessons
The RAG Pipeline
3 lessons
Agentic RAG
3 lessons
Frequently Asked Questions
- Do I need Docker?
- Yes — for running the Qdrant vector database locally. Setup instructions are included.
- Can I try it before paying?
- The first few lessons are free to preview. Full access requires Pro.
- What is the difference between RAG and fine-tuning?
- RAG pulls in context at query time. Fine-tuning changes the model itself. RAG is faster to build and easier to update — that's what we focus on.
- Will I learn LangChain?
- We use LlamaIndex for the final pipeline, but you'll understand each component first. You'll know what frameworks hide.
Build RAG That Actually Works in Production
Master the core building blocks of RAG — from embeddings to agentic retrieval.
Chosen by engineers moving AI out of prototypes
The Demo Trap. Sound familiar?
The 10-Line Illusion
You copied a 10-line tutorial. It worked perfectly for the sample PDF, but completely failed when you pointed it at your real company docs.
Hallucinated Context
Your RAG pipeline retrieved the wrong chunks, so the LLM confidently hallucinated a refund policy that cost your business money.
The "Lost in the Middle" Problem
You stuffed 20 pages of context into the prompt, and the LLM completely ignored the crucial paragraph hidden on page 12.
Black Box Frameworks
You used a framework's pre-built retrieval chain. Now it's returning garbage results, and you have no idea how to debug the vector search.
The fix isn't another LangChain wrapper.
It's understanding the primitives.
Senior engineers aren't better because they know more syntax. They're better because they've:
Once you understand the math and the architecture, debugging RAG becomes standard engineering.
No magic.
Just engineering.
We tear away the abstractions. You will build vector searches from scratch before we ever touch a framework, guaranteeing you know exactly how the underlying system actually works.
The RAG Architecture Build Path
From raw embeddings to an autonomous search agent.
The Math of Meaning
You generate embeddings and manually calculate cosine similarity to understand how semantic search actually works.
Vector Stores
You deploy Qdrant via Docker and build a scalable database for millions of document vectors.
Smart Chunking
You design splitting algorithms that preserve context overlap so the LLM never loses the semantic thread.
Agentic RAG
You build an autonomous agent that dynamically decides whether to query your vector DB or search the live internet.
From Embeddings to a Production RAG Pipeline.
Why RAG?
Understand why LLMs need external knowledge and how RAG solves the problem
Welcome to RAG Fundamentals
Workshop overview, the Green Bites scenario, and environment setup
The Problem with LLMs
Understand the three critical limitations that make RAG necessary
RAG — The Open-Book Exam
Understand how RAG works and compare it to other approaches
Embeddings
Learn how text becomes numbers and build semantic search step by step
What Are Embeddings?
Learn how embedding models convert text into numerical vectors that capture meaning
Measuring Similarity
Use cosine similarity to compare embeddings and find related content
Building Semantic Search
Build a working semantic search engine over the Green Bites menu
Vector Stores
Store and search embeddings at scale using Qdrant vector database
Introduction to Qdrant
Set up Qdrant vector database and understand collections, HNSW, and vector parameters
Storing and Searching
Upload vectors with payloads to Qdrant and query them
Hybrid Search with Filters
Combine vector similarity with metadata filtering for precise results
Chunking Strategies
Break documents into optimal pieces for embedding and retrieval
Why Chunking Matters
Understand why large documents must be split before embedding
Recursive vs Semantic Splitting
Compare structure-based and meaning-based chunking approaches
Choosing the Right Strategy
Learn when to use each chunking approach and how to avoid common pitfalls
The RAG Pipeline
Build a complete RAG system with LlamaIndex, source attribution, and guardrails
Building with LlamaIndex
Connect documents, Qdrant, and an LLM into a complete RAG pipeline
Source Attribution and Grounding
Verify that answers are backed by real sources and understand relevance scores
Custom Prompts and Guardrails
Control the personality and safety boundaries of your RAG system
Agentic RAG
Add reasoning and tool use to your RAG system with the ReAct pattern
From Pipelines to Agents
Understand why fixed pipelines fall short and how agents add reasoning
Building an Agentic RAG System
Create a ReAct agent with RAG and web search tools
Wrap-Up and Next Steps
Review everything you built and explore what to learn next
Detailed Curriculum
See every system, every week, in detail before you decide.
Amateurs paste code. Engineers build systems.
Scripts are easy.
Anyone can call `VectorStoreIndex.from_documents()` and pray it works.
Production RAG is hard:
- Design dense vs sparse retrieval hybrid strategies
- Implement multi-document summarization architectures
- Build source-attribution guardrails
- Architect sliding-window context retrieval
Stop building unreliable chat wrappers. Build resilient search systems.
Learn From Someone Who Actually Does This
I am the Head of Engineering at Jobbatical (EU Tech), with 8+ years of leadership and 15+ years of total experience in the software industry.
"Most engineers are not blocked by ability, but by lack of real system ownership."
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Frequently Asked Questions
Do I need Docker?
Can I try it before paying?
What is the difference between RAG and fine-tuning?
Will I learn LangChain?
5 hours from now:
Stop trusting magic. Start building engineering systems.