Stella and Amy

AI Evals and Analytics Podcast

News EN ↓ 3 episodios

Build trustworthy AI products through evaluation-driven development. Each episode covers practical evaluation strategies, industry trends, and best practices for building safe, reliable AI systems. From dataset generation and evals metrics design to cross-functional collaboration and post-launch analytics, we talk about how to build trustworthy and lasting AI products with a good AI evals and analytics framework. Subscribe for practical techniques, industry insights, and guest interviews on AI evaluation and analytics. More about AI Evals and Analytics -- https://ai-evals.org/ We (Stella & Amy...

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Autor

Stella and Amy

Categoría

News

Web del podcast

open.firstory.me

Último episodio

10 de mar. de 2026

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Episodios

From AI Evals to Business Impact 10.03.2026

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 thr...

Build AI Evals from Scratch: When and How? 07.02.2026

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 th...

AI Evals Skills: Why Data Scientists Have a Natural Advantage 26.01.2026

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...

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