Find Practical AI Wins
Identify narrow, repeatable AI workflow opportunities where reliability, cost, speed, and accuracy can be measured.
Course
Intro to AI Evals
Nate Grahek AI Educator @ The Rundown University
Learn how to measure AI quality, compare models, and trust workflows before you scale them.
6 chapters · 1h 17m
About this course
A beginner-friendly course on building a first AI evaluation with a spreadsheet, Claude Code, known outcomes, and a practical accuracy-and-cost scorecard.
What you'll learn
6 outcomesDesign Reliable Evals
Build workflow evals using real inputs, expected outputs, prompts, models, and answer keys from historical human decisions.
Choose Automation Targets
Select automation candidates with clear categories, meaningful volume, business value, and low-risk human-in-the-loop deployment paths.
Measure Model Tradeoffs
Compare prompts and models on the same task to balance output quality against latency and operating cost.
Boost Eval Accuracy
Improve accuracy with targeted business rules, examples, context, and decomposed workflow steps.
Preserve Workflow Knowledge
Capture tacit human expertise and manage AI context so workflow knowledge stays usable across experiments.
Course curriculum
1 part · 6 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.