Google

Amazon Web Services

Suncorp Group

Great Eastern

Jason Tan

Bootstrapped AI Founder | Author & Speaker | AI Product & Business Advisor 

Build a Ruthless AI Researcher to Evaluate 108 SaaS tools

Build an "Extract, Batch, Evaluate" sequence to stop AI memory limits and process huge datasets on autopilot.

Build a 3-Step Autonomous AI Research Workflow

Write a constraint prompt that forces AI to admit what it doesn't know, instantly filtering out fake data.

Eliminate AI Hallucinations with the "Verification" Rule

Map out your team's tedious manual tasks and translate them into strict, repeatable AI systems.

Delegate Your Operational "Dirty Work" to AI

Gain hands-on mastery of AI research tools and frameworks to deliver trusted insights faster while maintaining the rigor that matters.

AI Research Expert | Johns Hopkins PhD

Capital One

Bloomberg

Pinterest

John Whalen, PhD

PhD. O'Reilly author. Host of AI for UX, where I interview AI tool founders.

July 2026

September 2026

The researchers I talk to feel two things at once: overwhelmed by the pace of AI, and watching real possibilities open up that didn't exist two years ago.Both are true. AI lets you run 500 interviews across 12 countries in a week. It surfaces patterns across thousands of sessions. It opens research to teams who never had budget for it. Synthetic users let you pressure-test ideas in hours. These are new ways of working.But how do you trust the output? Which tools deliver, and which produce confident nonsense?That's the shift this course is built around: from doing research one study at a time to orchestrating systems that scale. You'll get hands-on with the platforms researchers actually use (Listen Labs, Coloop, Marvin, Synthetic Users), hear from the founders building them, and build the judgment to know what holds up.This is Course 1 of 2 in the Certified Agentic Research Specialist program — your foundation. Course 2, Agentic AI for Research, builds on it. The researchers pulling ahead are orchestrators — conducting research with more reach, impact, and confidence than before.No hype. Just what works.

Create Customer Insights with AI You Can Actually Trust

Conduct AI-moderated customer interviews confidently

Extract insights from 50+ interviews using AI-powered analysis tools

Develop frameworks to assess new AI tools and know when they actually work

Use synthetic users for rapid testing and validation of ideas before launch

Guide your team or organization in adopting AI research tools and best practices

Connect with practitioners navigating AI research together and sharing insights

You've worked with customer insights in some capacity—whether you've conducted research yourself, collaborated with researchers on your team

Experience with customer research or user feedback

This is a practical course with weekly exercises using AI platforms. You'll need time to experiment and apply what you learn.

Willingness to try new tools hands-on

We use tools and prompting techniques that don't require any coding knowledge. If you can write clear instructions, you're ready.

No technical background required

Researchers seeking to deliver insights faster at scale using AI—while maintaining the quality and rigor that makes their work trustworthy.

PMs and research leaders balancing the need for rapid customer insights with concerns about maintaining quality, rigor, and team standards.

Designers seeking practical AI tools to understand users, test concepts, and make evidence-based design decisions with confidence and speed.

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

Founder | CPO | 10 years in Product

Finastra

Trading Central

Printify

Madalina Turlea

Product leader with 10+ years experience in FinTech | Founder &CPO Lovelaice

2x founder | CTO | 14 years in Tech

Teleclinic

Nilo

SIXT Group

Freeletics

Catalina Turlea

Founder & CEO @Lovelaice | Co-founder & CTO @ nilo | 14 years in tech

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

Use AI Without Losing the Judgment Your Work Depends On

Human-AI Work & Systems Advisor

Joyce Ventures

University of Wisconsin-Madison

Center on PBIS

Joyce White Nelson 

Systems-Change Advisor | Human Judgment & AI-Assisted Work | Founder, iQuoteHero

Founding Live Workflow Lab — September 2026

AI is becoming part of everyday work, but using it well requires more than knowing which tool or prompt to use.For experienced professionals, the harder questions are often: What should I delegate to AI? What still needs my judgment? What should I verify? What am I still accountable for? And is this way of working strengthening my expertise—or slowly replacing parts of it I still need?In this practical workshop, you’ll bring one real workflow, decision, client deliverable, communication, or recurring task from your own work.Together, we’ll examine where AI adds genuine value, where human judgment and context remain essential, where rework or risk may be hiding, and how to redesign the workflow with clearer roles for both you and AI.You’ll leave with a practical Human + AI Workflow Map you can use again as the technology and your work continue to change.This is not a tools class. We work on your actual work.

Redesign one real AI-assisted workflow so you know what to delegate, what to verify, and where your judgment still matters.

Choose the right role for AI 

Find the judgment points 

Build in verification

Spot hidden rework and risk

Redesign one real workflow

Use the method again

Experienced entrepreneurs and founders Already using AI and want clearer judgment, better workflows, and less hidden rework.

Consultants, coaches, and advisors Use AI in client work but want to protect quality, credibility, voice, and professional responsibility.

Leaders, educators, and team facilitators Need ways to decide where AI helps, where humans stay accountable, and people keep learning.

A practical workshop on evaluating AI agents before launch

Founder & CEO | Ex-Adobe, WF, eBay | HBS

Adobe

Wells Fargo

eBay

Ganesan Anand

Founder, Formerly AI Product & Tech Leader (Adobe, Wells Fargo, eBay) · HBS alum

Next Cohort

<blockquote>Move your AI features from a vibe check to a value check. “Did the agent do the right thing? Would I catch it if it didn’t?” Every team shipping AI features is asking some version of these questions without a reliable way to answer them. An agent doesn’t just say the wrong thing. It does the wrong thing, and teams often find out from a user.Typical AI training falls into two traps: abstract theory that never leaves the slide deck, or disconnected techniques that stop short of a production-ready system. This workshop bridges that gap. I build enterprise infrastructure for AI evaluation every day, and I’ve seen that real adoption only happens when teams can trust their AI to perform consistently.You’ll learn a repeatable method for applying Responsible AI in practice, with a focus on reliability and safety. You’ll learn which metrics matter for your product, identify the edge cases that undermine trust, define how the product should respond, and prove it works before customers see a problem.</blockquote>

Learn a repeatable evaluation framework for building and releasing reliable AI features.

Know exactly what to measure

Find failures before your users do

Get a pass/fail read before release

Keep quality from slipping after launch

Access to leading commercial and open-source AI models is provided during the session for the eval exercises. Nothing to set up or pay for.

Model access is provided

Come ready to dig into how AI features fail and how to catch it. That mindset is all it takes.

Curiosity about AI quality

The session is live and remote, so a solid connection matters. Everything runs in-browser, and the no-code evaluation platform is provided.

Stable internet

AI engineers and product managers building, evaluating, or preparing to release AI features

Founders turning an AI prototype into a reliable product

Engineering and product leaders accountable for AI quality, safety, and release decisions

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: Building an AI Researcher to Evaluate Tools

Identifying the Right Operations for an AI Agent

Defining Specific Requirements for the Customer Support Tool

The Dirty Work Dilemma: Delegating Mundane Tasks

Why Standard Chatbots Fail at Long Evaluations

Selecting the Right AI Tools and IDEs

Overview of the 3-Step Automated Workflow

Phase 1: Extracting Data and Building a Master List

Phase 2: Batch Processing to Avoid AI Hallucination

Phase 3: Merging Results and Auditing the Data

Reviewing the Automated Workflow Architecture

Benefits of Using an AI Agent for Research

Key Takeaways for Building AI Workflows

Q&A: Creating Master Prompts and AI Skills

Operations professionals who optimize processes and manage day-to-day business functions.

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

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

Automating professional workflows with AI tools — productivity, operations, and task automation.

Build a Ruthless AI Researcher to Evaluate 108 SaaS tools

What you'll learn

Why this topic matters

You'll learn from

Jason Tan