Grounded AI Answers
Use RAG to ground AI answers in current, specific source material instead of relying on model memory or guesswork.
Build Reliable AI Assistants: Your RAG System Guide
Taught by Nate Grahek
Build reliable RAG assistants that ground answers in searchable documents, metadata, vector retrieval, and verifiable sources using practical Supabase and n8n workflows.
What you'll learn
RAG Use Case Validation
Validate a use case with a small file-based prototype before investing in a full custom RAG system.
Knowledge Base Design
Design chunks, metadata fields, and source references that make retrieved information accurate, traceable, and useful.
Search Strategy
Choose between keyword search, semantic search, and structured queries based on whether users need exact matches, meaning-based retrieval, or calculations.
Vector Database Setup
Build a Supabase vector database that stores text chunks, embeddings, and metadata for reliable retrieval.
Reliable Chat Workflows
Configure retrieval, re-ranking, and prompting so assistants answer consistently with evidence-backed source details.
Course curriculum
1 part · 7 chapters
About Nate Grahek
AI Educator @ The Rundown University
Nate is a SaaS founder and Fractional CMO who helps product-driven businesses build marketing systems that actually work — without the fluff. He's spent years helping founders and operators cut through marketing complexity and put the right things on autopilot.
At Rundown University, Nate brings that same hands-on, no-jargon approach to AI education. His workshops and courses focus on practical automation and AI workflows you can deploy the same day — no engineering background required. If you've ever wanted to use AI to get your time back, Nate shows you exactly how.
