RAG: Build AI Systems With Custom Knowledge
RAG (Retrieval-Augmented Generation) lets AI models access and reason over your custom data — documents, databases, knowledge bases — instead of relying only on training data. It is the foundation of enterprise AI. Our courses teach you to build RAG systems from scratch.
Courses
Start with any course below. The first 3 lessons of every course are free.
RAG & Vector Search
Build AI that knows your data. Embeddings, vector databases, chunking, retrieval, and hybrid search.
The Convergence Lab
The capstone. Build your own AI twin — persistent memory, autonomous agents, and human-AI convergence. This is where it all comes together.
AI Systems Design
Architecture patterns for production AI. Reliability, observability, cost optimization, and scaling.
AI Infrastructure & DevOps
Production AI systems. Edge functions, databases, caching, monitoring, and cost optimization at scale.
Frequently Asked Questions
What is RAG?
RAG (Retrieval-Augmented Generation) is a technique that gives AI models access to external knowledge. Instead of relying solely on training data, the AI retrieves relevant documents from your data and uses them to generate accurate, grounded responses.
Why is RAG important?
RAG solves AI hallucination by grounding responses in real data. It lets you build AI systems that know about YOUR business — your docs, products, policies — without expensive model fine-tuning.
What are vector databases?
Vector databases store data as mathematical representations (embeddings) that capture meaning. When you search, they find semantically similar content — not just keyword matches. Popular options include Pinecone, Weaviate, and Supabase pgvector.
Do I need to know machine learning for RAG?
No. Our courses teach RAG using pre-built embedding models and vector databases. You need basic coding skills (Python or JavaScript), but no ML expertise.
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