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What is Evaluation-driven development? When should you start building evals for your product? How to build it from scrach?

Using a real-world example of a customer chatbot for a medical insurance company, we walk through the process of setting up evals from scratch: translating product requirements into quantifiable metrics, curating quality test datasets (hint: you need fewer examples than you think), and making go/no-go decisions based on eval scores.

You'll learn why accuracy and safety require different approaches, how to avoid the trap of AI-generated test data, and why 94% vs 95% accuracy matters less than you'd expect—but safety guardrails are non-negotiable. This is the practical blueprint for anyone building AI products who wants to catch problems before users do.

00:00 – Introduction: Why We Need to Talk About Evals Now
00:39 – When to Start AI Evals?
03:20 – Example Setup: Medical Insurance Customer Chatbot
04:30 – Defining Evals in Product Requirements
07:19 – What Is Evaluation-Driven Development?
08:27 – Breaking Down "Accuracy": What Does It Really Mean?
09:42 – Dataset Curation: Quality Over Quantity
11:24 – How Big Should Your Test Set Be?
12:25 – Safety Guardrails: Knowledge Boundary and PII Leakage
15:29 – Making Release Decisions with Eval Metrics
17:33 – Start with What's Critical to Your Use Case

Stella Liu: https://www.linkedin.com/in/wenxingl/
Amy Chen: https://www.linkedin.com/in/amy17519/

More about AI Evals and Analytics -- https://ai-evals.org/

We (Stella & Amy) created the AI Evaluation & Analytics Playbook, a practical framework that helps teams ship production-ready, trustworthy AI systems.

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