Amazon

Adobe

TikTok

Google

Harpreet Kaur

Product @Microsoft , CEO - Why Blend In, Certified Leadership Coach 

Why Your AI Demo Impressed Everyone and Shipped Nothing

Microsoft

Salesforce

Product Leader- Microsoft , CEO- Why Blend In, Certified Leadership Coach 

Evals, edge cases, cost, latency, trust, support - see how each one kills launches, with real anonymized stories.

The six things every AI demo hides

Use the provided pattern - quality bar, failure mode, escalation - to turn "we'll evaluate later" into a testable spec.

Write your first three eval criteria this week

Locate which of the six gaps is blocking your ship date, and leave with the one artifact that reopens the conversation.

Diagnose why your feature is stuck and the fix

Build a real AI agent, find where it breaks, and improve it with evals you can trust, working the full loop hands-on.

ML Engineer with 25 years of experience

Airbnb

GitHub

DataRobot

AlixPartners

Hamel Husain

ML Engineer with 20+ years of experience.

ML Systems & Applied AI Evals Researcher

UC Berkeley

Stanford University

Shreya Shankar

ML Systems Researcher Making AI Evaluation Work in Practice

Cohort 5

Cohort 6

Save 25% ($1,050) on September's cohort <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/parlance-labs/evals/6/join?promoCode=LOOK-AT-YOUR-DATA">with this link</a>.--- 🏆 The most comprehensive AI evals course available. We've refined this material with over 4,500 engineers and PMs from teams like OpenAI, Google, Meta, Amazon, and Microsoft. <a target="_blank" rel="noopener noreferrer nofollow" href="https://testimonial.to/ai-evals-course/all">Read what students say →</a>📚 View <a target="_blank" rel="noopener noreferrer nofollow" href="https://docs.google.com/document/d/1KSUlzN-C8z1q_gHduCi6eKgCzlHVV6u3dT0aypqi_YI/edit?usp=sharing">the syllabus here </a>More than a course: an ongoing system, tools, and community for shipping better AI.<ul><li>🎮 A private Discord community for ongoing support, even after the course.</li><li>🤖 6 months of access to our AI Evals assistant.</li><li>♾️ Lifetime access to all recordings, materials, and future cohorts.</li><li>💬 10+ hours of live office hours to get your questions answered.</li></ul>Do you catch yourself asking any of these while building AI applications?<ol><li>How do I test outputs that need subjective judgment?</li><li>If I change the prompt, how do I know I am not breaking something else?</li><li>Where should I focus my efforts? Do I need to test everything?</li><li>What if I have no data or customers? Where do I start?</li><li>What should I measure, and what tools should I use?</li><li>Can I automate evals, and how do I trust it?</li></ol>If so, this is for you. All sessions are live and recorded.📝 We've completely refreshed the material for our Sep 2026 cohort. Scroll down to see the syllabus.

 Build, evaluate, and improve AI agents that work in production.

Build an agent you can measure

Find what's broken with error analysis

Build evaluators you trust

Catch regressions with CI/CD

Red-team for safety before users do

Improve accuracy and cut cost with evidence

Engineers and PMs who ship prompt changes and hope nothing breaks. (You'll learn to measure impact before and after every change.)

Teams still spot-checking AI outputs by hand instead of measuring systematically. (You'll learn how to build automated evals you can trust.)

Leaders who don't know where their AI is failing or where to invest resources. You'll learn how to systematically find &amp; prioritize issues.

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

AI Founder & Advisor to F500s | Ex-AWS

Amazon Web Services

University of Oxford

Aishwarya Naresh Reganti

Applied AI @ OpenAI Codex | Ex-Google

Databricks

Samsung

Kiriti Badam

June Cohort

September 2026 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 3000+ 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.

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

July

September

<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

You stop arguing about whether the agent is good, and start showing the number, the rubric behind it, and the decision it justifies.

Author, AI Agent Evaluation (Springer)

Abdullah Mansoor

Co-author of AI Agent Evaluation (Springer 2026), written for non-coders.

Cohort 1

You shipped an AI agent. It demos beautifully. Then a board member, a customer, or your own gut asks the question you have been avoiding: how do you know it works? Most teams answer with more testing, more prompt tuning, and a dashboard nobody trusts. The gap does not close, because the missing piece is not effort. It is a system: a written standard for what good means, an instrument that measures against it, and a loop that runs on a schedule. This is the companion course to AI Agent Evaluation (Mansoor and Bokhari, Springer 2026), cut for the person who makes the release decision rather than writes the code. Six modules take you from naming the failure you are afraid of, to writing a contract your agent has to honor, to scoring it on four dimensions, to running a loop that improves it every week. Every tool, framework and metric is introduced at concept level. After a module you can hold your own with your engineers using these terms. No lesson asks you to open a terminal. You leave with seven artifacts built on your own agent: a contract, a rubric with anchors, a benchmark set, a failure taxonomy, a release gate, a cost and latency budget, and a seven-step playbook.

A demo proves it worked once. Production asks you to prove it works every time.

Name the failure pattern your agent is most likely to hit

Write a one-page agent contract

Score your agent on the four dimensions that decide readiness

Build a rubric two independent raters would score the same way

Run the Biruni Loop so quality improves on a schedule

Put cost and latency on the same row as quality

Every exercise is built on your own agent, so you leave with a contract and a rubric for something real rather than a worked example.

An AI agent in production, or one you are about to ship

Everything is taught at concept level. You will hold your own with your engineers using these terms without opening a terminal.

No coding required, and no prior testing or evaluation background

The templates only pay off against a decision you actually have to make, so bring a release you are genuinely unsure about.

Willingness to bring one real agent and one real release decision

Founders whose agent demos beautifully, then answers the same question two ways on a Tuesday. You need the rate, not the anecdote.

Product leads who own the AI feature but not the code. Engineers report a score. You cannot tell if the product improved or the test eased.

Technical founders who stopped coding. You read a system diagram fine. You want the vocabulary back at concept level, without a code tour.

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

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.

Executives and senior leaders who set strategy, lead teams, and drive organizational outcomes.

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

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

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

Operating and deploying large language models in production — fine-tuning, inference, monitoring, and cost optimization.

Why Your AI Demo Impressed Everyone and Shipped Nothing

What you'll learn

Why this topic matters

You'll learn from

Harpreet Kaur