Users are bombarded with AI-powered products every day, but most can’t tell which ones actually work.

In a market flooded with AI marketing claims, trust and proof are what set great products apart.

To stand out, you need data to back up your product’s credibility, not just a clever demo.

That is where AI Evaluations and Analytics come in.

47% of organizations have already faced AI gone wrong (McKinsey, 2025). Even leaders like OpenAI and Anthropic are self-insuring because traditional coverage cannot keep up.

AI systems are unpredictable. The real moat today is how well you evaluate and iteratively improve them.

👉 About This Course

You don’t need another coding course.

This is a methodology and framework course for product and data leaders shaping the next generation of AI products.

Learn a battle-tested framework to evaluate, monitor, analyze and improve your AI products — from testing first prototype to monitoring and iterating in production.

Make AI decisions backed by evidence, not intuition.

You’ll apply the AI Evals & Analytics Playbook — a proven system that combines human and automated tools for both scalability and reliability.

Through real-world examples, exercises, and demos, you’ll learn how to:

✅ Design evaluation workflows that align with your product across different development stages

✅ Set up pre-release evaluations and assessments to enable faster, data-driven iteration

✅ Build post-release monitoring and analytics that keep your AI systems reliable and accountable

By the end, you’ll walk away with an actionable, organization-ready evaluations and analytics plan for your own AI product.

🗓️ Course Plan

This accelerated program condenses four weeks of content into two for a fast, immersive learning experience.

Lesson 1: AI Evaluations - Why & What

- Getting buy-in on AI Evals

- Build your first AI Evals team

- Differentiate Eval, Analytics, and XAI

- Integrate AI evaluation into your product lifecycle

Exercise: Section 1 of your playbook

Lesson 2: How to Evaluate AI Products

- AI Evals & Analytics Framework Overview

- Human evaluation vs. Automated evaluation

- Use LLM-as-a-judge

- Design rubrics and metrics

Exercise: Section 2 of your playbook

Lesson 3: Experiment Design and AI Eval Tools

- Evaluator model selection

- Curate or generate test sets

- Experiment design

- Overview of popular eval tools

Exercise: Sections 2 & 3 of your playbook

Lesson 4: Monitoring & Analytics

- Handle user data

- Product monitoring practices for AI products

- Understand leading & lagging indicators

- Conduct post-launch review and analytics

Exercise: Sections 3 & 4 of your playbook

🎁 Founding Cohort Benefits (Oct 27 - Nov 9) Join our first cohort and help shape the course. Receive personalized guidance, in-depth discussions on your evaluation challenges, and access to our professional community of product leaders, data practitioners, and AI builders.