Build a Fully Working Production RAG System from Scratch
Tutorial RAG demos work on 10 documents. Production RAG handles 10 million. Learn the engineering that bridges that gap.
$49–$99 — PDF + code repository
Get NotifiedThe Problem
Every RAG tutorial uses the same 5 PDF files. Production RAG needs to handle millions of documents, mixed formats, and continuous updates — none of which tutorials cover.
Retrieval quality degrades silently. Without proper evaluation, your RAG system returns plausible but wrong answers and you have no way to detect it.
Chunking, embedding, and retrieval each have dozens of options. Without production experience, you waste weeks on configurations that don't matter.
What's Inside
End-to-end RAG engineering from document ingestion to production monitoring.
Chunking Strategy Guide
Fixed, semantic, recursive, and parent-child chunking with benchmarks on real data.
Embedding Model Selection
Comparative analysis of embedding models — cost, quality, and latency trade-offs.
Vector Database Deep Dive
Pinecone, Weaviate, Qdrant, pgvector — when to use what and migration patterns.
Retrieval Optimization
Hybrid search, re-ranking, query expansion, and multi-index strategies.
RAG Evaluation Framework
Faithfulness, relevance, and completeness metrics with automated testing.
Production Deployment
Scaling, caching, monitoring, and cost optimization for production RAG.
Who This Is For
AI Engineers
Building RAG-powered applications that need to work reliably at scale.
ML Engineers
Adding retrieval capabilities to existing LLM applications and pipelines.
Engineering Managers
Evaluating RAG architectures for your team's AI products.
Get Notified When It Launches
Early-bird pricing + first access when the course launches.
Common Questions
When will the course be available?
We're targeting Q2 2026. Sign up to get notified the moment it launches — plus early-bird pricing.
How is this different from the free RAG guide on the site?
The free guide explains RAG concepts. This course teaches you to build a production system — with code, benchmarks, evaluation, and deployment patterns.
What vector databases are covered?
Pinecone, Weaviate, Qdrant, Chroma, and pgvector. Each with setup guides, trade-off analysis, and migration patterns. Plus a decision framework for choosing the right one.
Do I need ML experience?
Basic Python and API experience is enough. You don't need to understand how embeddings work internally — just how to use them effectively in a RAG pipeline.
What format is it?
PDF guide + companion code repository with a fully working RAG system you can deploy and customize.
Don't miss the launch.
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