Amplitude AI Analytics

Agent Evals 101: Best Practices for AI Product Teams

Amplitude

Included Health

Optimizely

Oracle

Vinay Goel

Staff AI Engineer at Amplitude

Ken Kutyn

Solutions Engineer at Amplitude with 10+ years in experimentation & AI evals.

Skip this and you'll scale problems, not performance. Here's what breaks and how to spot it early.

Why agent evals can't wait

Walk away with a practical structure you can apply to agent-driven workflows this week.

A starting eval framework for AI product teams

See how teams evolve from basic checks to a systematic, scalable measurement process.

A 3-6 month maturity path for your eval practice

Learn how to evaluate, ship, and monitor GenAI products that perform reliably in production.

Cohort 4

Head of AI Applied Science

Shopify

Carvana

Harvard University

Stella Liu

Cofounder, AI Evals & Analytics

System1

Uptake

Seasalt.ai

UCLA

University of Washington

Amy Chen

Cofounder at AI Evals & Analytics

Cohort 3

<blockquote>Building AI is easier than ever. Building AI that is reliable, safe, and trusted is not.AI evaluations help teams measure quality, detect failures, and build guardrails around your GenAI systems that will survive in the real world.</blockquote><blockquote>🔹</blockquote><h3>Who this course is for</h3>Data scientists, PMs and QA/QE professionals who:<ul><li>have no prior background in AI evals, or</li><li>have tried LLM-as-a-judge but want a clearer framework.</li></ul><blockquote>🔹How the course works</blockquote><ul><li>Self-paced lectures you can watch anytime</li><li>Hands-on exercises using a pre-built GenAI product</li><li>Dedicated office hours for questions and guidance</li></ul><blockquote>🔹</blockquote><h3>What makes this course different</h3><ul><li>Playbook-driven approach</li><li>Evaluation-driven development</li><li>Product-focused</li><li>Hands-on experience</li><li>Flexible learning + expert support</li></ul>🔹Curious about the ideas behind this course?You can explore related topics in the <a target="_blank" rel="noopener noreferrer nofollow" href="https://datasciencexai.substack.com/">Data Science × AI Substack</a>, where we share perspectives on evaluating real GenAI products.🔹💰 Purchasing Power Parity pricing adjustments upon request. ✉️ Email us: StellaxAmy@gmail.com

Learn How to Evaluate GenAI Products Confidently in 3 Weeks

Execution-ready Testing & Monitoring Playbook

GenAI Risk Management Strategy

Hands-On AI Evals Exercise

Product LeadersBuilding GenAI products and need a clear playbook for evaluation frameworks, success metrics, and shipping with confidence.

Data ScientistsLeveling up with AI product skills: learn AI evals, LLM-as-a-judge, and design metrics for GenAI systems.

QA and Test EngineerAdapting traditional testing practices to GenAI systems.

Acquire and develop a critical skill for product managers who are leading and contributing to AI products.

GenAI Product Builder @ Google, ex-Meta

Marily Nika, Ph.D AI/ML

Product Manager AI&ML @ Google

Diego Granados

Senior Data, ML and GenAI Scientist

George Zoto

Senior NLP/AI Scientist

Cohort 5

“Does it work?”... “Is it good enough?”... “Can we ship it?”...How do you answer these questions for AI products? You’re responsible for “running evals” but what does that mean?How do you choose the right metrics, interpret fuzzy results, and make a confident decision?This course gives you a framework to do just that.<ul><li>Map user value to evaluation (eval) objectives so your metrics aren’t abstract. Define success then translate it into measurable criteria.</li><li>Choose metrics you can actually maintain: capability, safety, UX friction, latency, cost and “does this reduce support tickets or increase activation.”</li><li>Set ship/no-ship thresholds you can defend to leadership.</li><li>Build lightweight workflows that work in real teams: human review where it matters, automation where it lasts, documentation that drives decisions.</li><li>Consider domain constraints (e.g., healthcare safety) and know what to avoid: silent failures, misleading proxy metrics and tests that don’t reflect production.</li><li>Tie everything to ROI: impact vs unit cost, eval coverage vs reliability, and the minimum viable monitoring you need post-launch.</li></ul>Experience AI evals through a case-based approach with a real AI product that we evaluate together.

Eliminate uncertainty in shipping AI features

Make confident ship or hold decisions for AI features

Translate user value into clear evaluation goals

Choose the right metrics for capability, safety, UX, and cost

Set defensible thresholds leadership will trust

Build lightweight evaluation workflows teams can maintain

Navigate domain constraints and avoid common failures

PMs leading AI features, growth, or platform initiatives

PMs who partner with ML teams and want to set evaluation standards

PMs who need to make clear “ship or hold” calls without doing the engineering

Build a full AI evals suite in 3 hours on a real use case

Founder @Lovelaice | 10 years in Product

Finastra

Trading Central

Printify

Madalina Turlea

Product leader with 10+ years in platform and SaaS products | Founder Lovelaice

Founder @ Lovelaice | 2x founder & CTO

Teleclinic

Nilo

SIXT Group

Freeletics

Catalina Turlea

2x founder & CTO | 14+ years in tech | Founder @Lovelaice

June

<blockquote>Whether you're building your first AI feature or exploring what AI could do in your product, there's a moment every PM hits: you look at the AI output and think "this seems okay?" but you don't have a structured way to know for sure.You've heard about evals. You know vibe checking isn't enough. But where do you actually start?This workshop takes you from that starting point to a working evaluation suit, built on a real AI feature use case, in 3 hours.You'll write a production system prompt, run it across multiple LLMs, read and annotate real AI responses to understand where they break, find failure patterns through error analysis, define automatic evals based on what you found, and measure whether your improvements actually worked.You'll leave with hands on experience on building your own evals suite, failure taxonomy, and a repeatable process to evaluate and improve any AI feature from here.Every participant works hands-on annotating AI responses, writing evals and running improvement iterations. We've run 1,500+ AI experiments with product teams and this workshop is the compressed version of what we've seen work.</blockquote>

Design, build, validate and iterate on an AI feature, hands-on

Understand the most important components of building AI features at scale

Build an evaluation suite to scale your testing

Learn how to identify failure patterns. Then iterate on both prompt and evals

The PM who wants hands-on experience on how to build high performing AI features

The PM who is overwhelmed by the technical tools and process around building AI

The product lead who wants improve the iteration speed or wants to scale manual testing with automatic evals

Evals aren't your product moat. A continuous improvement flywheel is.

AI Founder & Advisor to F500s | Ex-AWS

Amazon Web Services

Microsoft

University of Oxford

Aishwarya Naresh Reganti

June Cohort

Check out our free AI Evals Certification <a target="_blank" rel="noopener noreferrer nofollow" href="https://github.com/aishwaryanr/awesome-generative-ai-guide/blob/main/free_courses/ai_evals_for_everyone/README.md">here</a> (5000+) Learners!Most teams building AI products today know about evals and LLM judges, but they’re deeply confused about how to actually turn them into actionable improvement signals post production. Just throwing in LLM judges won’t magically fix your product. 🚨 You can also upgrade to our 1-year <a target="_blank" rel="noopener noreferrer nofollow" href="https://levelup-labs.ai/ai-builder-pass">AI Builder Pass</a>, which includes unlimited retakes, access to all our other courses, live sessions and partner credits from companies like Vapi AI, Arize, Cursor, Vercel, OpenAI, and more! Register for this course, and we’ll send you upgrade details over email.<ul><li>This course shows how to use offline evals, online evals, and production monitoring to drive continuous improvement, based on real production experience.</li><li>This course is for people who’ve already built AI systems and are stuck with evals that are noisy, expensive, or uselessIf you need something more foundational, check out our top-rated flagship<a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/aishwarya-kiriti/genai-system-design"> enterprise AI course</a> taken by over 1500+ builders and leaders.</li><li>We follow a flipped-classroom format with async lecture content and meet twice a week for office hour style sessions to optimize two-way/interactive live time.</li></ul>

Move beyond one-time evals to continuous improvement flywheels for AI products

Understand what evals actually mean and why it’s misunderstood

Build a pre-deployment evaluation foundation from scratch

Choose the right types of metrics for the system you’re building

Monitor in production and close the improvement loop

You should have built small AI applications before and be familiar with concepts like RAG, MCP, and tool use.

A good understanding of AI system design

The course includes optional Python assignments, you're free to use coding agents, but basic coding skills are recommended

Working knowledge of Python/Coding (Optional)

Software/AI Engineers, Strategists, Data Professionals, Solution Architects and Consultants looking for systematic evaluation practices

Business Leaders and Product Managers seeking to gain the technical understanding of AI Product Lifecycle and intentional iteration

Founders &amp; teams stuck at systematic evaluations and monitoring, unsure how to turn signals into real product improvements.

The Agent-Powered Super IC

Agentic AI

Coding with AI

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Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

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Prototyping

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Leadership

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Machine Learning

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Context Eng

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Figma

Design Systems

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Brand

B2B Marketing

SEO & AEO

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Communication

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Exec Presence

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Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Product managers who drive strategy, development, and launch of products.

Engineers and engineering leaders who build, deploy, and scale software systems.

Data scientists who apply statistical and ML techniques to extract insights from data.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Designing scalable, reliable software systems — architecture patterns, distributed systems, and technical tradeoffs.

Measuring and evaluating AI model performance, quality, and reliability for production use cases.

Agent Evals 101: Best Practices for AI Product Teams

What you'll learn

Why this topic matters

You'll learn from

Vinay Goel

Ken Kutyn