Build Reliable AI Assistants: Your RAG System Guide
Workshop recording
Nate Grahek AI Educator @ The Rundown University
7 chapters · 47m · Intermediate
About this course
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
- Grounded AI Answers Use RAG to ground AI answers in current, specific source material instead of relying on model memory or guesswork.
- 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
Your instructor
Nate Grahek AI Educator @ The Rundown UniversityNate 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.