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Hands-on, first-principles micro-courses on designing, evaluating, and deploying generative AI systems.

Autoraters 101 Featured Course
● Available Now 3 Video Lessons Interactive Playground 6 Python Templates

Autoraters 101: The Crash Course

The complete guide to LLM-as-a-judge evaluation systems. Stop relying on subjective manual reviews or broken traditional metrics. Learn why larger models can objectively rate smaller production pipelines, calibrate rubrics against human reference sets, and run repeatable automated evals.

Lesson 1 · 49s Why human review stops working the moment you ship GenAI features.
Lesson 2 · 57s The 3 pillars of LLM judges: The Food Critic, Post-Hoc Context, and Model Asymmetry.
Lesson 3 · Demo & Code Live interactive rubric scoring demo + complete 6-file Python eval repo.
Includes: Gemini 2.5 Flash judge code, test cases, and Cohen's Kappa agreement check
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Upcoming Tracks

Course 4 · Coming Next

Production LLM Monitoring & Drift

Calibrating autoraters in CI/CD pipelines, tracking statistical drift over time, and catching subtle model degradations before users do.

In Development

RAG Blueprints & Grounding

Building hallucination-free retrieval pipelines, deterministic context assembly, and automated factuality filters for enterprise data.

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