AI Evals and Analytics Podcast

Why do most AI teams only ask "is this actually working for the business?" after it's too late? When should you start connecting evals to business impact and how do you actually do it? Using the same medical insurance chatbot from the last episode, we show how to bridge the gap between model metrics and the outcomes your leadership actually cares about. We introduce the Eval-to-Impact Stack: a three-layer framework that connects eval metrics, product metrics, and business metrics. More details are available in our Substack post: From AI Evals to Business ImpactInterested in AI Evals and Analytics Playbook course? Here is an exclusive discount for our listeners 00:00 – Introduction & Recap of Episode 2 00:53 – Why Teams Ask the Business Impact Question Too Late 01:38 – The Stat: 95% of Enterprise AI Pilots Fail 01:58 – The Translation Problem: Model Metrics vs. Business Metrics 02:38 – Why Evals Get Labeled as Overhead (And How to Fix It) 03:16 – The Eval-to-Impact Stack: Three Layers Explained 05:00 – Applying the Framework: Insurance Chatbot Walkthrough 07:13 – Work Backwards from Business Goals, Not Forward from Metrics 08:05 – The Cross-Functional Superpower: Speaking Both Languages 08:25 – Closing: "Build the Product Right" vs. "Build the Right Product"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

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 Now00:39 – When to Start AI Evals?03:20 – Example Setup: Medical Insurance Customer Chatbot04:30 – Defining Evals in Product Requirements07:19 – What Is Evaluation-Driven Development?08:27 – Breaking Down "Accuracy": What Does It Really Mean?09:42 – Dataset Curation: Quality Over Quantity11:24 – How Big Should Your Test Set Be?12:25 – Safety Guardrails: Knowledge Boundary and PII Leakage15:29 – Making Release Decisions with Eval Metrics17:33 – Start with What's Critical to Your Use CaseStella 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

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