Claude

ChatGPT

LangChain

CrewAI

Microsoft

Sol Farahmand

AI Engineer | 2X Entrepreneur | AI Hackathon Winner

How To Validate Your AI Product Before You Build

MindStudio

Lovable

AI Hackathon Winner | 2X Entrepreneur | AI Workflow Builder

AI moves fast, but bad assumptions move faster. Skipping validation is why most projects fail before they ever ship.

Why skipping a POC is the costliest AI mistake

We apply the Value Proposition Canvas, Lean Canvas, and Customer Journey Map directly to AI projects with a real exampl

Frameworks that structure your AI thinking

Use OKRs, customer discovery, and first principles thinking to test if your idea has legs before you commit to a build.

How to validate your idea without building it

Every framework we cover gets organized into a repeatable playbook during the session and take into your next project.

Build a reusable POC playbook from scratch

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

Take one real problem from the first interview to the shipped prototype, using AI at every step. Leave with proof you can do the job.

Product Owner @ JPMC | Ex-Amazon

JPMorgan Chase & Co.

Amazon

State Street

Nexus Adv

Nijat Hajiyev

Data Product Owner @ JPMorgan | Ex-Amazon, Ex-State Street | CBAP, PMP, PhD c..

Cohort - Sep'25

Learn to turn problems to the products in 2 months

Cohort 2 - Aug'26

Every week another tool promises to do your job. AI writes the user story. AI drafts the BRD. AI maps the process in nine seconds.So you learn the tools. You take the course, collect the certificate, memorize the framework. And you are still the person who documents what someone else already decided.What did not change: someone has to sit in a room of people who disagree, find the real problem under the noise, and decide what gets built. AI made that person more valuable. It made everyone else optional.This course builds that person!You take one real problem, yours or one I give you, and drive it end to end: elicitation, root cause, process models, data, architecture, business case, prototype, pitch. AI is not a module here. You use it in every session, and you learn exactly where it lies to you.My students have moved from finance, marketing and support into analyst and product roles at KPMG, Velux, Docplanner and Revolut. Not because they memorized a framework. Because they had something to show.Built for people getting into the role, not people already in it.

AI writes the docs now. Your value is knowing which problem to solve.

1. Find the real problem before anyone writes code

2. Model how the business actually works, not how people say it works

3. Get your own data instead of queuing behind the data team

4. Talk to engineers without losing the room

5. Build the thing, don't just describe it

6. Defend the decision in front of people

You are a BA or product owner who documents decisions made by others. You want to own the problem, not transcribe it.

You work in finance, ops, marketing or consulting. You know how a business makes money. You have nothing that proves you can build.

You are an engineer, data analyst or QA, tired of building whatever the vague ticket says. You want to define the problem, not receive it.

AI lets you build fast  >>> The real risk is building the wrong thing.

15+ years Product&Marketing, 100+ MVPs 

Miro

LEGO®

Elena Avramenko

15+ yrs in Product & Marketing, award-winning founder, 100+ new product launches

Cohort 4

Cohort 5

Vibe-coding tools like Lovable and Claude let you go immediately from idea to prototype. What used to take weeks now takes hours. That sounds like an advantage — but it's created a new problem: vibewasting - building fast without clarity or a plan.Building the wrong thing faster just gets you to the wrong place sooner😉.Most vibe-coders skip the one step that determines whether any of it is worth it: validation. They start building the moment they have an idea — before they've talked to a single customer, before they know if the pain is real, before they know if anyone would pay.This workshop fixes that. In 2 hours you'll build a clear plan for the next 14 days — giving you much more confidence in your idea and helping you justify further investment of time and money. We'll start by writing a sharp bet on your idea, define exactly who your first customer is, and design interview questions that surface real pain. You'll also create a Lovable prompt that specs the tiniest version worth building - something you can vibe-code in an hour and put in front of real people to validate. You leave with a 14-day validation plan, ready to test before you invest weeks building the wrong thing.

Validation steps and framework to identify the best idea to invest your time to build

Have clarity on what ideas to keep and what to keep

A first-time founder or side-project builder with a business idea who wants to test it before spending weeks vibe-coding the wrong thing.

A vibe-coder who has already built something that didn't get traction and wants a structured approach before starting their next idea.

A professional or domain expert who spotted a problem in their industry and wants to validate it as a business before learning to build.

 In one day, validate YOUR AI Opportunity & leave with a 90-day plan.

ICF MCC · MBA ESSEC · 6,500+ sessions 

Judith Glory

ESSEC MBA · ICF MCC · Executive Coach · 6,500+ sessions

Stop debating about AI. In one day, validate your opportunity and leave with a 90-day plan.

Cohort 2

The pressure to act on AI is real. But most leaders are making decisions based on hype, vendor pressure, or fear of being left behind — not on evidence.The result is predictable: pilots that fade, budgets wasted on tools that never get used, and leadership teams that keep debating instead of deciding.This workshop exists because there is a better way. In one structured day, you apply a proven framework to your team's specific reality — not AI in general, not someone else's use case. You evaluate what you do today. You identify where AI could genuinely help. You name every risk and price it. And you make a clear go or no-go call you can stand behind.Not every team should move forward with AI right now. If your numbers say no, this workshop will tell you that clearly — and give you the conditions under which yes becomes the right answer. A No-Go decision backed by data is worth more than a yes driven by pressure.

Managers and leaders navigating the AI moment who need clarity, not inspiration — and a plan they can defend to senior leadership team.

Team managers, VPs, and senior leaders who sense the AI opportunity but need evidence to act — or to confidently say not yet.

Leaders responsible for team output who are ready to validate their AI opportunity once and for all — no tech background needed.

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

Introduction: The Million-Dollar Question of What to Focus On

Meet the Speaker and Essential AI Research Tools

Agenda Overview: The Startup Playbook

The Cautionary Tale of Bob's Restaurant: Why Research Matters

The Five Crucial Questions to Ask Before You Build Anything

Product-Market Fit: A Scientific Approach to Finding Your Niche

Validating Your Concept: Is It a Painkiller or a Vitamin?

Crafting Your Problem and Value Proposition Statements

The Value Proposition Canvas: A Matchmaking Tool for Business

The Lean Canvas: Mapping Your Entire Business Model

Understanding the Customer Journey Map

First Principle Thinking: Building a Better MVP by Analyzing Competitors

Using OKR Tables for Accountability and Goal Setting

Conclusion and Resources

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

Startup founders building new businesses, from MVP to scale.

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

Identifying opportunities and validating what to build through customer research, experimentation, and prioritization.

Finding and validating product-market fit — identifying underserved markets, testing hypotheses, and iterating to fit.

How To Validate Your AI Product Before You Build

What you'll learn

Why this topic matters

You'll learn from

Sol Farahmand