Diagnose Bad Outputs
Diagnose weak AI outputs by identifying missing context, poor task fit, vague goals, or overloaded conversations.
Context Engineering: The Core Skill Worth Learning Even As Models Improve
Taught by Nate Grahek
Build reliable AI workflows by supplying stronger context, structuring inputs with Markdown and JSON, managing conversation limits, and turning vague prompts into repeatable collaboration patterns.
What you'll learn
Provide Better Context
Improve results with examples, business details, constraints, success criteria, and trusted source material before rewriting prompts.
Control the Workflow
Guide AI through interview-style workflows, approved steps, and numbered options to keep collaboration focused and efficient.
Manage Conversation Limits
Manage context limits by chunking complex work, summarizing progress, starting fresh chats, and reusing saved project materials.
Structure AI Work
Structure inputs and outputs with Markdown or JSON so AI can follow priorities and produce predictable results.
Match Tools to Tasks
Choose the right setup for deep research, recurring projects, RAG-style knowledge bases, and focused coding plans.
Course curriculum
1 part · 8 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.
