Finastra

Printify

Trading Central

Madalina Turlea

10+ years leading products in FinTech

Lovelaice

October 2025

March 2026

February 2026

April

In this lightning session you will learn to:Identify your AI feature's specific evaluation metricsMove beyond generic metrics to specifics that measure the ways your AI feature fails for your specific use case How to choose between deterministic and LLM-based evaluationUnderstand when to use zero-cost deterministic checks vs. AI-as-judge, and why you should start with deterministic first Build your first automated evaluationSet up practical deterministic checks: format validation, schema checks, length limits, and value ranges

Setting up your first evaluation 

Overwhelmed by the AI hype when trying to build products that actually work?&nbsp;While everyone on LinkedIn claims to have the "magical AI solution," the reality is that 80% of AI projects fail due to inconsistent results and lack of systematic testing. Discover the one proven solution—systematic experimentation—that separates successful AI implementations from the failures, and learn the framework that delivers consistent, valuable results without the technical complexity.

AI Experimentation Intro


By the end of 4 weeks, you'll have the skills, the methodology, the tools and the hands-on experience to lead AI initiatives at your company

Co-founder @ Lovelaice, Product Leader

Co-founder @Lovelaice, 10+ years in Product

Founder @Lovelaice | 14+ years in Tech

Catalina Turlea

Founder @Lovelaice, 14+ years leading Tech

Build and evaluate your first AI feature

Full-stack developer

Frontend Developer

A 4-week hands-on course where Product Managers learn to test prompts, benchmark models, write evals and build business cases for AI features using real data on your real use-case.Most PMs are stuck between two extremes: either waiting for engineering to figure it out, or shipping features that fail in production because they only tested on two happy cases.There's a better way.This course teaches you a systematic methodology for building AI features that actually work - before you write a single line of production code.You'll learn how to test prompts across multiple models, evaluate results like a pro, and make data-backed decisions about what to ship.This isn't theory.You'll run real experiments on your own product ideas using the same approach that's helped teams catch critical failures before their customers did, discover that cheaper models often outperform expensive ones, and ship AI features with confidence instead of crossed fingers.By the end of 4 weeks, you'll have the skills, the methodology, the tools and the hands-on experience to lead AI initiatives at your company - not just participate in them.

Stop guessing. Start testing. Ship AI with confidence.

How to integrate AI into your product

Build a eval pipeline for your AI features

You want to be in the top 5% of PMs who actually understand how to build with AI

You're tired of waiting for engineering to lead AI initiatives.You feel pressure to "do something with AI" but don't know where to start

You've shipped an AI feature but aren't confident it's working well (or your customers are finding the bugs)

Build your first AI eval in 3 hours — on your own use case, with your own data.

Founder @ Lovelaice | 2x founder & CTO

Co-Founder @ Lovelaice | 2x founder & CTO

Founder @Lovelaice | 10 years in Product

Co-founder & CPO @ Lovelaice | 10+ years in Product

<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 framework, built on your use case, with your data, in 3 hours.You'll write a 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 metrics based on what you found, and measure whether your improvements actually worked.You'll leave with your own accuracy metric, a failure taxonomy for your use case, and a repeatable process to evaluate and improve any AI feature from here.Every participant works hands-on on their own use case. We've run 1,000+ AI experiments with product teams and this workshop is the compressed version of what we've seen work.</blockquote>

The PM who owns an AI feature but not the process around it

The PM about to pitch an AI feature internally

The product lead responsible for a whole team's AI quality

From AI user to AI feature designer: a live exercise

Founder @Lovelaice

Use a live diagnostic to see how your AI evals approach compares to 100+ teams and where your biggest gap is.

Get your personalized AI product evaluation score

See the exact same AI output through two lenses and learn why the designer's lens is what ships better products.

Shift from AI user to AI feature designer

Turn your gut feeling about AI quality into structured criteria you can apply to any AI feature tomorrow.

Build an evaluation framework from your own reactions

Setting up your first AI eval with a LLM-as-judge

Understand why most teams' LLM judges don't work and the specific mistakes that make them unreliable.

Most common mistakes to avoid when building an LLM-as-judge

Learn how to identify what to check through LLM-as-judge, define specific rules, and build the judge prompt

How to write your judge instructions

Know how to evaluate the evaluator by comparing judge scores to human labels and decide if you can trust the results.

How to evaluate your LLM-as-judge

Setting up your first AI evaluation

Move beyond generic metrics to specifics that measure the ways your AI feature fails for your specific use case

Identify your AI feature's specific evaluation metrics

Understand when to use zero-cost deterministic checks vs. AI-as-judge, and why you should start with deterministic first

How to choose between deterministic and LLM-based evaluation

Set up practical deterministic checks: format validation, schema checks, length limits, and value ranges

Build your first automated evaluation

How to build AI beyond the classic chatbot

You'll understand why the "add a chatbot" default happens and when it's actually the wrong choice for your product

Why most teams default to chatbots

You'll get a practical framework for identifying where AI can enhance your product without adding a chat interface

"The framework: ways to integrate AI beyond chat interface

You'll learn how to evaluate which AI integration pattern fits your users' workflow, not just copy what others are doing

How to choose the right AI integration for your product

How to test AI when you don't have any data yet

Learn how to create test user inputs for your golden dataset to validate your AI features when starting from scratch

How to create test cases when you're starting from zero

Learn how to manually evaluate AI responses for your AI feature to define the expected output

What 'good' looks like for your specific use case

Learn how to run their prompt across multiple models, select the best outputs, and create a reusable test dataset

Build a reusable test dataset (no engineering)

Lessons from one year of AI product building

Avoid the mistakes I made in the beginning and that most PMs make when starting to add AI to their products

Top mistakes to avoid in AI product building

Insights into what actually works

The actual framework I use to ship AI features at Lovelaice

Madalina Turlea Co-founder at Lovelaice - AI Feature Analytics

Madalina Turlea

About Madalina

Courses

Build and evaluate your first AI feature

Workshops

AI Evals for Product Managers

Lightning Lessons

From AI user to AI feature designer: a live exercise

Setting up your first AI eval with a LLM-as-judge

Setting up your first AI evaluation

How to build AI beyond the classic chatbot

How to test AI when you don't have any data yet

Lessons from one year of AI product building

Resources

Setting up your first evaluation

AI Experimentation Intro

Madalina Turlea Co-founder at Lovelaice - AI Feature Analytics

Madalina Turlea

About Madalina

Courses

Build and evaluate your first AI feature

Workshops

AI Evals for Product Managers

Lightning Lessons

From AI user to AI feature designer: a live exercise

Setting up your first AI eval with a LLM-as-judge

Setting up your first AI evaluation

How to build AI beyond the classic chatbot

How to test AI when you don't have any data yet

Lessons from one year of AI product building

Resources

Setting up your first evaluation

AI Experimentation Intro

How to build AI beyond the classic chatbot