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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What are the skills required for AI evals? Why data scientists have a natural advantage in AI evals? 

Evaluating AI isn’t just about "vibe coding" with an AI assistant. It actually requires a solid foundation in statistics for picking sample sizes and coding to build your own testing frameworks. Data scientists have a huge head start here because they are already pros at designing metrics and communicating risks. 

In the augural episode, we also explain why Evals (pre-launch testing) and Analytics (post-launch user feedback) are two sides of the same coin: one makes sure the AI works, and the other makes sure people actually love using it.

00:00 – Introduction to AI Evals & Analytics 
01:31 – Why Data Scientists Have a Natural Advantage
01:59 – Technical Pillar: Statistics 
02:48 – Technical Pillar: Coding & Prompt Engineering 
05:03 – Technical Pillar: Dataset Generation 
08:35 – Soft Skills & Stakeholder Collaboration 
11:17 – Domain Expertise in Regulated Industries 
15:50 – New Skills for the GenAI Era 
19:25 – Why Evals and Analytics Must Come Together 

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.

Powered by Firstory Hosting