Teresa Torres

World Pulse

Board Member

Product Talk

I help digital product teams adopt continuous product discovery practices including a regular cadence of customer interviews, rapid prototyping, and assumption testing. I emphasize strong critical thinking practices to ensure there is a strong connection between what the teams are learning in their research activities with the product decisions that they are making. 

Recent clients include: Allstate, CarMax, CapitalOne, Snagajob, Spotify, among others.

Product Discovery Coach

Master of Science in Learning & Organizational Change at Northwestern University

Instructor, Designing Solutions for Organizational Effectiveness

Executive Learning & Organizational Change Program at Northwestern University

Instructor, Co-Creating Change

Kindling

Advisor

AfterCollege

- Relaunched www.aftercollege.com to move away from traditional search and toward career exploration and discovery to better meet college students needs. 
- Repositioned employer products away from transactional job listing-based recruiting and toward on-campus relationship-building recruiting.
- Grew engaged users by 65% month over month and reduced audience churn by 3x.
- Redesigned product email strategy tripling MAU in first quarter.
- Introduced user-centered, hypothesis-driven product development practices.
- Launched content marketing strategy targeting college students (blog.aftercollege.com) and employers (employers.aftercollege.com).

Vice President, Products

Affinity Circles

Affinity Circles was acquired by Mingle LLC. 

Affinity Circles operates exclusive social networks for over 150 university alumni associations, professional organizations and honor societies, connecting 18 million members.

President & CEO

- Led an operational turnaround by focusing on core products, implementing new sales and account management processes, growing a key channel partner relationship and reducing operating expenses. 
- Extended operating runway from months to sustained profitability.

Vice President, Operations

- Drove product vision, strategy and execution for 2 revenue-generating product lines, including a social networking platform used by Stanford, Johns Hopkins, Purdue and 150+ other organizations and a corporate recruiting solution used by Google, Merrill Lynch, UBS and dozens of other Fortune 500 clients.
- Drove product discovery through client interviews, user research, usability studies and traffic analysis. 
- Managed a 4-person team of product managers and an interaction designer.

Senior Director, Product Management & User Experience Design

Become, Inc.

Become.com provides shoppers with product research and price comparison.

- Led design on core product from site launch to 3.5 million monthly unique visitors.
- Drove product discovery through user research, usability studies and site traffic analysis.
- Led design and development of key-performing features from concept to launch: “Suggest as You Type” Search Suggestions, “My Favorites” shopping cart, innovative shop filters including Find by Color and community-based shopping lists.

Product Manager / UI Designer

HighWire Press

HighWire Press, a division of the Stanford University Libraries, is the online publisher for over 1400 journals, books, reference works and other scholarly publications.

- Lead interaction designer for several Knowledge Environments (i.e. research collaboration communities) for the American Association for the Advancement of Science (AAAS), Oxford University Press and the International Bone and Mineral Society.
- Managed client design projects from concept to launch.
- Drove the design and implementation of several core features that became part of the Knowledge Environment platform including a suite of personalization services (i.e. saved searches, research folders, etc) and an innovative virtual journal that pooled topical articles from multiple publications.
- Implemented a robust template system that allowed diverse Knowledge Environments to maximize shared code while accommodating customizations.

Interaction Designer

As a product discovery coach, I help product teams make better decisions about what to build. I teach them how to:
- Identify clear business outcomes 
- Uncover unmet customer needs, pain points, and desires
- Discover the right solutions that deliver both business value and customer value

My book Continuous Discovery Habits has sold over 100,000 copies, we've taught over 15,000 students from 102 countries through the Product Talk Academy, and reach 100,000s of product people through our free blog posts and social media content.

If you aren't satisfied with how your teams decide what to build, get in touch.

I help digital product teams adopt continuous product discovery practices including a regular cadence of customer interviews, rapid prototyping, and assumption testing. I emphasize strong critical thinking practices to ensure there is a strong connection between what the teams are learning in their research activities with the product decisions that they are making. 

Recent clients include: Allstate, CarMax, CapitalOne, Snagajob, Spotify, among others. 

My personal assistant is hard at work preparing my day.

As a product discovery coach, I help product teams make better decisions about what to build. I teach them how to:
- Identify clear business outcomes 
- Uncover unmet customer needs, pain points, and desires
- Discover the right solutions that deliver both business value and customer value

My book Continuous Discovery Habits has sold over 90,000 copies, we've taught over 13,000 students from 102 countries through the Product Talk Academy, and reach 100,000s of product people through our free blog posts and social media content.

If you aren't satisfied with how your teams decide what to build, get in touch.

Author, Speaker, Product Discovery Coach

I've been posting a lot about Claude Code. I'm a big fan.

Several people have asked if Anthropic is sponsoring these posts. The answer is no. I am simply a fan and I like to share what is working for me. 

I know there is a lot of buzz and hype around generative AI right now. I also know that it has completely changed the way that I work. I know for some of you it is also changing the way that you work. And I know others of you are curious. 

I know with every technological change some people get left behind—for a wide variety of reasons. I'm writing about what works for me as a way to help bring others along (if they want to come along). It's that simple. I have no other motivation. 

As part of this initiative, I've been hosting Claude Code Office Hours. My goal is to create a space for product folks to come together and share what they are trying, what's working, and what's not. These are casual chats. They are a lot of fun. They are not recorded. 

My next round of Claude Code Office Hours will be on December 16th at 9am PST and 4pm PST. Invites will go out to Supporting Members and CDH Members of Product Talk on Dec. 9th. If you are interested in joining, be sure to subscribe before then.

I'm diving deep into Anthropic's Interviewer data and methods. I'll have more to share soon. But on the surface, this looks really exciting. However, my Interview Coach has some feedback on the questions the AI asked. 😀 

https://lnkd.in/g7mU8ZFB

"My default stance is to try to do everything with AI. But still there are only so many hours in the day and I have to prioritize what to turn into a workflow vs. what to do as one-off tasks."

Learn how to strategically choose which tasks to automate or augment with AI. This article breaks down a practical framework for building your personal AI-enhanced operating system, based on five months of real-world experimentation.

Key takeaways include:

🎯 Three ways AI can multiply your impact: do more of what you're good at, eliminate friction in frequent tasks, and remove what drains you

❓ Seven critical questions to evaluate if a task is worth automating (frequency, complexity, enjoyment level, and more)

🔄 The difference between automation vs. augmentation and when to use each approach  

📝 50 real AI workflows across task management, writing, podcasting, coding, research, and discovery

✅ How to break complex tasks into components and decide what requires human judgment

Check out the comments for a link to the article. 

💭 What's one repetitive task you do that you think AI could help with? Share your thoughts in the comments below.

I've got a full lineup of events coming up in December. Let's end the year strong!

Business Fundamentals: Connect product decisions to business outcomes.
🗓️ Wednesday, December 3, 2025, 9am-10am PST
Leaders: Our product teams aren't impacting business outcomes. 
Teams: We have no idea how to impact business outcomes that feel far away from our day-to-day decisions.
In this webinar, Hope Gurion and I will discuss the often missing business context that helps product teams see how their day-to-day decisions can drive business outcomes.
Register here: https://buff.ly/HAzmKFC

The What and Why of Continuous Discovery
🗓️ Monday, December 8, 2025 9am-10am PST
In this webinar, we'll explore the habits of successful continuous discovery teams. You'll get a clear benchmark to aspire to and some practical tips on how to get there.
Register here: https://buff.ly/Q9OIyor

Continuous Interviewing: Unlock The Value of Story-Based Customer Interviews
🗓️ December 9, 2025 9am-10am AM PST
Most product teams now know they should be engaging with customers on a regular basis. But not all customer conversations are the same. There's a simple (but not always easy) technique you can learn to gain valuable insights from every customer conversation. Come learn why this technique matters and how to get started.
Register here: https://buff.ly/kOGWDEt

All webinars are recorded and the recording will be shared with registered participants for a limited time.

Claude Code Office Hours
🗓️ Tuesday, December 16, 2026, 9am-10am & 4pm-5pm PST
Come get help with anything I've written or shared about Claude Code. Connect with peers and share what you are learning. Only open to Supporting Members and CDH Members at Product Talk. Invites will go out by email on December 1st. If interested, subscribe before then. Office hours are not recorded. 

Let me know in the comments if you'll be joining any of the events. And please tag folks who you also think would be interested. I appreciate it.

"Claude Code has no undo button." 

When we give large language models access to our computers, there's inherent risk. This article walks you through how to safely use Claude Code across seven tiers of access—from reading files within a folder to installing third-party code and plug-ins.

You'll learn:
📁 How to monitor what Claude can access
⚠️ Which commands are safe vs. dangerous
🛡️ How to create your own "undo button"
🔍 How to vet packages before installation
✅ A safety checklist for every Claude Code session

Two key principles: 
1️⃣ Don't let Claude do anything on your local machine that you don't understand. 
2️⃣ And always create a safety net—undo is your friend.

Find a link to the full article in the comments.

💬 For those using Claude Code (or similar CLI tools): What's been your biggest "wait, should I approve this?" moment? Share your thoughts in the comments below.

What happens when you combine a real customer problem, a no-code prototype, and a team willing to listen to every single call?

In this episode of Just Now Possible, Teresa Torres talks with Steven Payne (Product Manager), Gabriel Stock (Senior Engineering Manager), and Philipe Steiff (Senior Software Engineer) from Perk—a company that helps businesses eliminate "shadow work" like travel booking and expense management. They share how they built a voice AI agent that calls hotels to verify virtual credit card payments, preventing travelers from arriving to find their rooms unpaid.

What started as a hackathon experiment in Make.com  became a production system handling over 10,000 calls per week across multiple languages. Along the way, the team learned hard lessons about prompt engineering for voice (numbers, pronunciation, and a very "Karen-like" first version), how to break a single monolithic prompt into structured conversation stages, and why listening to actual calls beats any amount of theorizing.

You'll hear how they:

- Built a working prototype without writing a single line of backend code
- Structured the call into discrete stages (IVR, booking confirmation, payment) to improve reliability
- Created two eval systems: one for call success classification, another for conversational behavior
- Scaled from five calls a day to tens of thousands per week while maintaining quality
- This is a detailed look at building AI for real-time human interaction—where the stakes are high and the feedback is immediate.

Show Notes
Guests
- Steven Payne, Product Manager, Perk
- Gabriel Stock, Senior Engineering Manager, Perk
- Philipe Steiff, Senior Software Engineer, Perk

What we cover in this episode
- How Perk's team identified an AI use case by connecting prior experimentation with a real operational problem
- Why they chose Make.com for prototyping—and shipped to production without touching backend code
- The evolution from a single prompt to structured conversation stages (IVR handling, booking confirmation, payment request)
- How breaking up the agent's task dramatically improved reliability
- Building two eval systems: classification for success rates and LLM-as-judge for conversational behavior
- Why the team still listens to calls manually even with automated metrics
- The challenge of prompt engineering for voice: numbers, booking references, and text-to-speech markup
- Lessons learned from expanding to German (prompts in native language improve results)
- How this project uncovered other operational problems they didn't know existed

Resources & Links
- Perk
- Make.com – No-code automation platform used for the prototype
- Twilio – Voice/telephony provider
- Eleven Labs – Text-to-speech provider (used in early experiments)

Listen on Spotify, Apple Podcasts, or watch on YouTube. Links are in the comments.

I'm really struggling to get Claude to use skills. It just tries to do everything itself without invoking the appropriate skill. 

I am finding that slash commands and agents work better. Am I the only one? 

If you are using skills, what tips do you have for getting Claude to actually use them. I've already written descriptions that tell Claude exactly when to use the skill.

"LLMs are trained to be helpful. They want to give you an answer. But sometimes the right answer is 'I don't know'—and getting an LLM to admit that turns out to be surprisingly hard."

Through conversations with product teams shipping real AI features to real customers, fascinating patterns are emerging across different industries. Here's what teams are discovering as they build AI products in production:

🤝 Small, cross-functional teams are shipping fast
🎓 Nobody started as an AI expert
🧠 Domain expertise drives product decisions  
🎯 Starting narrow beats starting broad
🏗️ Architectures are getting more sophisticated—but in different ways
📊 Evals evolve from simple to sophisticated
🛠️ The trendy tool isn't always the right tool for the job
🔗 The "infinite integration problem" shows up everywhere
❓ The challenge nobody expected: Knowing when to say "I don't know"

Stack Overflow built four versions of their AI-powered search, then killed it. But those lessons directly informed their next product, which was successful. Teams iterate rapidly and aren't afraid to pivot.

The teams that are succeeding share key traits: They start with real customer data and real problems. They build evaluation into the process from the start. They're ruthlessly focused on specific use cases rather than trying to solve everything.

Check out the comments for a link to the article.

🤔 What's been your biggest surprise when building or using AI products? Share your thoughts in the comments below.

I am so looking forward to this. I was planning to go as an attendee in 2025, but was barely walking by June. So excited to be in the lineup for 2026.

Product Leadership Coach, Author of STRONG product people & STRONG product communities, Curator at Product at Heart

✨ We’ve locked in our first three keynotes for Product at Heart 2026 and I couldn’t be more excited!

Christian Idiodi, Teresa Torres, and Elaine Kasket are joining us next June — and this is just the beginning of what’s shaping up to be an amazing conference lineup.

If you’re thinking of attending, now’s the time: early bird tickets are still available. (Link in the original post!)

I’m super excited for this workshop. Come join us in Hamburg next June.

Ready to build your own personal operating system using AI? 🤖

Teresa Torres is bringing her unique AI Maker Studio workshop to Hamburg on June 25, where you'll learn how to automate and augment your work using AI command-line tools like Claude Code and ChatGPT Codex.

In this hands-on workshop, you'll learn how to: 
⚡ Evaluate which tasks are best for AI automation vs. augmentation 
⚡ Design effective workflows that save you time and make you better at your job 
⚡ Prioritize which tasks to tackle next 
⚡ Develop routines to continuously invest in your personal operating system

You'll walk away with a solid start on your personal operating system and be equipped to keep building it out in the weeks and months ahead.

Teresa is an internationally acclaimed author, speaker, and coach who teaches a structured approach to continuous discovery. She's coached hundreds of teams at companies from startups to global enterprises and has taught over 16,000 product people through the Product Talk Academy. She's the author of Continuous Discovery Habits and and a dear supporter of Product at Heart since day one.

Tickets on sale now: https://lnkd.in/emxNVbgc

Building an AI Sleep Coach: How Rest is Making CBTI Principles Accessible to DIY Sleep Hackers | Just Now Possible

What if you could get personalized sleep coaching—inspired by the same principles that cost thousands of dollars and have year-and-a-half waitlists—through a voice AI that checks in with you every morning?

In this episode of Just Now Possible, Teresa Torres talks with Martin Siniawski (CEO and co-founder) and Ignacio Perez (CTO) from Rest about how they built an AI sleep coach inspired by Cognitive Behavioral Therapy for Insomnia (CBTI) principles. The journey started when they noticed users of their podcast app were listening to content to fall asleep, explored sleep audio solutions, and eventually pivoted to an AI-powered voice coach when LLMs emerged.

They share how they evolved from basic chatbots to a sophisticated voice-first system with memory, dynamic agendas, and RAG—all while navigating the tricky line between wellness and medical products. Their "one bite of the apple at a time" approach to building AI offers practical lessons for teams tackling complex, personal AI products.

Show Notes
Guests
- Martin Siniawski, CEO and co-founder, Rest
- Ignacio Perez, CTO, Rest

You'll hear how they:
- Discovered the sleep use case from podcast app user behavior (10% of users, but high willingness to pay)
- Used jobs-to-be-done research to identify "DIY sleep hackers" as an underserved segment
- Chose CBTI (Cognitive Behavioral Therapy for Insomnia) as their foundation—a clinically proven approach with 80% efficacy
- Evolved from text chatbot to voice-first AI using Vapi for voice and OpenAI for reasoning
- Built a memory system that remembers user context (like traveling, having a dog) with time-based relevance
- Created dynamic agendas that drive daily conversations based on sleep data, program stage, and user compliance
- Managed parallel development paths (text via OpenAI Assistants and voice via Vapi)
- Moved from massive system prompts to RAG for general sleep knowledge, keeping user data in prompts
- Navigated wellness vs. medical product positioning with clear guardrails against diagnosis and medication advice
- Used weekly error analysis with domain experts (sleep therapists) to drive product iterations
- Built LLM-powered evals for safety boundaries and experimented with Hamming for voice testing

Resources & Links
Rest – AI sleep coach app
Vapi – Voice agent platform Rest uses
Langfuse – Observability and evals platform
Hamming – Voice testing platform
AI Evals Maven Course by Hamel Husain and Shreya Shankar (Get 35% off with Teresa's affiliate link)

Listen on Spotify, Apple Podcasts, or watch on YouTube. Links are in the comments.

Ever launch a new product feature that seemed solid—until a small, overlooked detail broke everything? In this episode, Teresa Torres and Petra Wille share their real-world experiences wrangling global invoicing and taxes while running small, international businesses.

What starts as a rant about EU tax compliance turns into a sharp product lesson: how failing to map the entire path to customer value—down to the tiniest regulatory requirement—can kill your product’s usefulness.

Whether you’re shipping code or selling courses, this conversation will remind you that sweating the details isn’t about perfectionism—it’s about ensuring your product actually delivers value at the moment that matters.

In this episode:
- The nightmare of global invoicing for small online businesses
- Why even big platforms (like Squarespace and Teachable) miss the mark on EU tax compliance
- How Petra and Teresa navigated invoicing across borders with Ableify and LearnWorlds
- The key difference between meeting regulations and meeting customer needs
- What product teams can learn from regulatory edge cases
- How missing a single detail can block the “moment of value creation”
- Why story mapping is critical for finding gaps between “we shipped it” and “customers got value”

Key takeaways:
- Customers define value, not your compliance checklist.
- Regulatory work still requires discovery—you can’t skip understanding user needs.
- The path to value doesn’t end when your feature works; it ends when your customer succeeds.
- “Sweating the details” isn’t micromanagement—it’s good product management.

Memorable quotes:
“If you don’t sweat the details, people choose other platforms.” — Petra Wille
“It’s not a little detail when your client won’t pay the invoice.” — Teresa Torres 

Find links to Spotify, Apple Podcasts, and YouTube in the comments.

🎙️Communities of Practice

In this episode of All Things Product, Teresa Torres and Petra Wille explore how product people can intentionally design their own communities of practice—and why it matters for long-term learning and growth.

Teresa shares her introvert-friendly approach to continuous learning: curating a personal learning network (PLN) filled with people she wants to learn from. Petra contrasts that with her more collaborative style of learning with others through small peer groups, hackathons, and local meetups. Together, they unpack how each approach supports curiosity-driven development, how to find your “definition of good” when starting something new, and the habits that make learning a deliberate practice.

Whether you’re an independent product coach or part of a product org shifting toward continuous discovery, this episode will help you rethink how you learn—and who you learn with.

Show Notes
Key Topics:
⭐ What a “community of practice” really means in modern product work
⭐ The difference between learning from people vs. learning with people
⭐ How to find like-minded peers for collaborative learning
⭐ Building your Personal Learning Network (PLN)
⭐ Personal knowledge management as a product skill
⭐ Why curiosity-driven learning builds stronger product intuition
⭐ How committing to talks, books, or courses drives deeper learning

Favorite Quotes:
💬 “Nobody on that list knows they’re in my personal community of practice.” — Teresa Torres
💬 “Sometimes you don’t know your new definition of good until you start learning.” — Petra Wille

Find links to Spotify, Apple Podcasts, and YouTube in the comments.

Give it a listen and share your thoughts in the comments below.💬👇

🎙️Customer Interview Analysis

In this episode, Petra Wille and Teresa revisit a topic they once strongly pushed back on: using AI to analyze (and maybe even synthesize) customer interviews. Six months and several experiments later, Teresa’s perspective has evolved — and the conversation opens up a nuanced look at where AI can genuinely help and where it can quietly erode your team’s customer understanding.
They dig into recent studies on how AI affects junior vs. expert performance at work, the difference between analysis and synthesis, and why your unpolished interview skills matter more than any shiny new AI workflow. Along the way, they share personal stories (including an unexpected gaming-PC detour) that illustrate how expertise shapes the usefulness of AI tools.
This is a candid, practical conversation for product teams wrestling with the real-world tradeoffs of integrating AI into continuous discovery.

Show Notes
In This Episode
🤖 Why their early stance on AI-powered interview synthesis has shifted
📝 What Teresa learned from running 15 interviews through ChatGPT and Claude
📈 How AI raises the floor for beginners but accelerates experts even more
🔍 The importance of separating analysis from synthesis
😅 Why most teams struggle with customer interview synthesis in practice
📚 What happens when interviews pile up — and whether AI can realistically help
⚠️ The risks of relying on AI when your interviewing skills aren’t solid yet
⚡ A vision for “expert + AI” synthesis that’s both fast and high-quality
🗣️ The ongoing debate about AI-led customer interviews
💻 A detour into PC-building that perfectly illustrates the limits of AI support

Key Takeaways
✨ AI isn’t magic. It can help, but only if your interviews are strong and you provide the right context.
👍 Beginner + AI is usually better than nothing. But the real performance gains come from expert + AI.
🧩 You still need to synthesize every interview individually. Dumping transcripts into an LLM isn’t a shortcut.
🏰 Customer understanding is a competitive moat. Outsourcing it entirely will cost you in the long run.
❤️ Empathy comes from human interaction. AI can’t replace the experience of talking directly to your customers.

Find links to Spotify, Apple Podcasts, and YouTube in the comments.

Give it a listen and share your thoughts in the comments below.💬👇

Lawrence Jones (Incident.io) explains why retrieval isn't just about "find similar stuff." Their AI SRE pre-processes each incident—alert type, impacted systems, symptoms—and runs similarity searches across multiple dimensions, then re-ranks the results.

Because with RAG, success comes down to one thing: how good you are at asking questions.

👉 Find a link to the full episode in the comments.

Rest is a voice AI product that guides users through a daily sleep diary and check-in. Over eight weeks, the AI coach supports, educates, and walks users step-by-step through what they call a "sleep makeover."

But the real work starts after: helping users sustain those changes and navigate life challenges that could trigger setbacks.

👉 Links are in the comments.

Each incoming message is preprocessed, categorized, and enriched with vendor data before any response is written. That structure lets Xelix's Helpdesk use LLMs with context, accuracy, and control.

👉 Links are in the comments.

This was a fun conversation. Does your discovery work inform your eval strategy?

ML Engineer with 20 years of experience helping companies with AI

It doesn't matter how good your AI evals are if you're solving the wrong customer problem.

In a recent discussion, Teresa Torres (author of Continuous Discovery Habits) explained why she views AI evals as "the next discovery habit."

The connection is twofold:

1. Discovery informs evals. Evals are often "garbage in, garbage out." If your test cases are based on guesses about user personas and needs, you're evaluating the wrong thing. Customer interviews reveal the real use cases, language, and scenarios that form the foundation of your test sets.

2. Discovery defines "good." For subtle failures like a "persona mismatch" in tone, an engineer cannot be the sole judge of quality. Discovery forces you to align quality with your customer.

This reframes evals from a purely technical task to a core part of product assumption testing.

Watch the full conversation here: https://lnkd.in/gKxsf_Dn

Rest's team discovered that voice is warmer than text—and that warmth matters when users are sharing something as private as their sleep struggles.

Voice creates intimacy. It builds a different kind of connection with the AI coach, one where trustworthiness becomes the foundation for real progress.

👉 Links are in the comments.

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