by Tom Leung
Product Management podcast where 20 year PM veteran Tom Leung interviews VP's, CPO's, and CEO's who rose up from product to talk about their careers, the art and science of product management, and advice for other PM's. Watch video on YouTube. firesidepm.co Learn more about host Tom Leung at http://tomleungcoaching.com <br/><br/><a href="https://firesidepm.substack.com?utm_medium=podcast">firesidepm.substack.com</a>
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January 19, 2026
<p></p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://firesidepm.substack.com?utm_medium=podcast&utm_campaign=CTA_1">firesidepm.substack.com</a>
January 5, 2026
<p>I met Albino Sanchez in the bleachers at a high school JV football game. While our sons battled it out on the field for Palo Alto High School, we found ourselves deep in conversation about something far removed from touchdowns and tackles: why some product leaders thrive while others crash and burn in seemingly similar companies.</p><p>Albino doesn’t fit the typical Silicon Valley mold. Born and raised in Mexico City, he spent his early career as a strategy consultant helping large companies implement frameworks like Balanced Scorecard and OKRs. But unlike most consultants who move on to the next engagement, Albino couldn’t stop thinking about his former clients. Some organizations flourished with these frameworks. Others abandoned them within months. The strategic tools were identical. The execution was completely different.</p><p>What he discovered would fundamentally change how I think about my own career moves—and it should change how you think about yours too.</p><p>The Pattern That Changes Everything</p><p>After years of looking back at his consulting clients, Albino noticed something remarkable: “Those organizations that were really thriving with these frameworks and really growing, they had a special type of leader. And that leader was usually a people-centered leader, a leader that was humble, that was a servant leader, and that this leader cared about their people, listened to them, and really wanted collaboration.”</p><p>This wasn’t just about nice leadership. It was about creating what he calls “the atmosphere for people to thrive.”</p><p>The insight hit him hard enough that he completely pivoted his career. He became an executive coach, spending the last 15 years working with leaders to shape healthier, more productive cultures. He moved his family from Mexico City to Palo Alto four years ago and recently founded Aha! Impact, a company focused on helping organizations achieve the right culture so both the business and employees can thrive.</p><p>But here’s what matters for you as a PM: Albino’s journey revealed something most of us learn the hard way. Culture doesn’t just influence whether a strategy succeeds. Culture IS the strategy.</p><p>Why “Culture Eats Strategy for Breakfast” Isn’t Just a Poster on the Wall</p><p>You’ve probably seen this quote attributed to Peter Drucker plastered on every startup’s office wall. But do you actually believe it?</p><p>Albino puts it this way: “We need to have the right environment so people can thrive and then implement and then be successful in business.” Without that environment, even the most brilliant product strategy becomes a document that sits in a Google Drive folder, gathering digital dust.</p><p>The Culture Paradox: Why Google, Amazon, Meta, and Microsoft All Win Differently</p><p>During our conversation, I pushed Albino on something that had been bothering me. If culture is so critical, how do companies with wildly different cultures all succeed? Amazon’s frugality and bias for action looks nothing like Google’s innovative freedom and psychological safety. Microsoft’s collaborative enterprise focus differs dramatically from Meta’s move-fast-and-break-things mentality.</p><p>His answer surprised me.</p><p>While different cultures can succeed, Albino sees clear patterns in what works today: “Innovation is one of them. We need to have nowadays with so many changes with AI, technology, globalization, communications. We need to be innovative. We need to be adaptive. We need to embrace change as something that’s part of our day to day.”</p><p>The successful organizations aren’t choosing between being people-centered OR innovative OR efficiency-driven. They’re becoming all three simultaneously. The old archetypes (pick your culture and stick with it) no longer apply in our rapidly evolving landscape.</p><p>But here’s the critical insight for PMs: You need to understand which cultural attributes matter most to you personally. Because while multiple cultures can succeed, not every culture will allow YOU to succeed.</p><p>The Real Reason You’re Miserable at Work</p><p>Albino shared something that hits close to home for many experienced PM’s: “People join organizations because of the company and they leave the organization most likely because of the boss.”</p><p>This tracks with every conversation I’ve had as an executive coach. The PMs who come to me aren’t struggling with their OKRs or roadmaps. They’re struggling with leadership dynamics, unclear values, and cultural misalignment.</p><p>Think about your own career. When you’ve been most energized, most productive, most creative. Was it because of the company mission statement? Or was it because you had a leader who created space for you to do your best work?</p><p>When you’ve been most miserable, was it really about the compensation or the commute? Or was it about a leader who micromanaged, who didn’t value collaboration, who created an atmosphere of fear rather than trust?</p><p>Culture doesn’t just make work more pleasant. It fundamentally determines whether you can bring your best self to the job.</p><p>The Leadership Styles That Shape Product Cultures</p><p>Here’s where Albino’s work gets really practical. He identifies four primary leadership archetypes that shape organizational culture, and understanding these can help you decode any company you’re considering:</p><p><strong>1. The Controlling Leader</strong> This leader centralizes decision-making, micromanages execution, and views team members as resources rather than collaborators. They might get short-term results, but they create cultures where PMs become order-takers rather than strategic partners. Innovation dies because risk-taking gets punished.</p><p><strong>2. The Competitive Leader</strong> Everything is a zero-sum game. Teams compete internally for resources, recognition, and rewards. This can drive individual performance but often at the expense of collaboration. For PMs, this means product launches succeed but platform thinking fails. You win your battle but lose the war.</p><p><strong>3. The Collaborative Leader</strong> This is Albino’s people-centered leader. They invest in relationships, foster psychological safety, and view success as collective rather than individual. In product organizations, this looks like cross-functional partnerships that actually work, user research that influences decisions, and retrospectives that drive real improvement.</p><p><strong>4. The Creative Leader</strong> These leaders embrace experimentation, tolerate failure, and push for innovation. They create cultures where PMs can propose bold ideas without fear. But without enough structure, these cultures can become chaotic.</p><p>The best leaders, and the best cultures, combine elements of all four, calibrated to the organization’s specific needs. As a PM evaluating a new role, you need to assess not just the stated values but the actual leadership style you’ll experience day-to-day.</p><p>The Questions You’re Not Asking in Interviews </p><p>Most PMs treat interviews as one-way evaluations. The company assesses you; you try to impress them. Albino argues this is backwards.</p><p>“This is a two-way assessment,” he told me. “You are also interviewing them.”</p><p>I know what you’re thinking: “Tom, that’s easy to say when you have options. When you’re desperate for a job, you can’t afford to be picky.”</p><p>I get it. But here’s the truth Albino helped me see: accepting a role at a company with cultural misalignment doesn’t solve your job search problem. It delays your job search problem by six months while making you miserable.</p><p>Your objective isn’t to get as many offers as possible. Your objective is to get offers from places where you’ll thrive.</p><p>So what questions should you actually ask?</p><p><strong>On Work-Life Integration:</strong> “How do you manage team collaboration across different locations and time zones?”</p><p>These aren’t just logistics questions. They reveal whether the company trusts employees or requires surveillance. They show whether leadership believes productivity comes from presence or output.</p><p><strong>On Decision-Making:</strong> “Tell me about a recent product decision where you had significant disagreement among stakeholders. How did you resolve it?”</p><p>This behavioral question (turned around on the company) reveals their true decision-making process. Do they rely on data, authority, consensus, or customer feedback? Do they value PM input or just expect execution?</p><p><strong>On Failure and Learning:</strong> “Describe a recent product launch that didn’t meet expectations. What happened, and how did the team respond?”</p><p>The answer tells you everything about psychological safety. Do they blame individuals or examine systems? Do they learn from failures or hide them?</p><p><strong>On Growth and Development:</strong> “How do PMs typically grow in their careers here? Can you share specific examples of PMs who’ve advanced and what enabled their growth?”</p><p>This reveals whether the culture actually invests in development or just talks about it in the handbook.</p><p>But here’s Albino’s most important advice: “It’s very important that you are authentic, you are yourself. Don’t try to make an act there. It’s very common to do that just to cover the expectations of the potential employer. But you know what? Try to get rid of that fear and try to be yourself.”</p><p>This is counterintuitive in a competitive job market. Every instinct tells you to mold yourself to what they want. But cultural misalignment has costs. Stress. Burnout. Short tenure. Another job search in six months.</p><p>Better to be yourself, assess fit honestly, and find a place where you can actually thrive.</p><p>How AI Is Changing Culture Assessment </p><p>Here’s where Albino’s work gets really interesting for those of us in tech. He’s building an AI-powered tool to help companies assess cultural fit during hiring.</p><p>Traditional culture fit assessment is notoriously unreliable. It often means “do I want to get a beer with this person,” which perpetuates homogeneity and bias. Or it gets delegated to a single interviewer who may not accurately represent the actual culture.</p><p>Albino’s approach is different. His tool analyzes the organization’s stated values, actual behaviors, and cultural attributes. Then it evaluates candidates against these dimensions through structured assessment.</p><p>“It’s going to analyze your organization, what are the values, and depending on your stage, your size, your location, what type of company you are, it’s going to analyze all this information and it’s going to recommend which are the key cultural factors or cultural behaviors that you need to assess when you interview a candidate,” he explained.</p><p>The tool is currently in beta testing, launching in January. But the concept matters even if you never use it: Culture fit should be systematic, not subjective. It should be measured, not assumed.</p><p>For PMs, this has implications beyond hiring. If companies can systematically assess culture, you can systematically evaluate it too. The questions you ask, the observations you make, the research you do before accepting an offer—these aren’t nice-to-haves. They’re essential.</p><p>The Framework: How to Evaluate Culture Before You Accept the Offer</p><p>Based on Albino’s expertise and my own painful lessons, here’s a practical framework for assessing culture fit:</p><p><strong>Step 1: Define Your Non-Negotiables</strong></p><p>Before you start interviewing, get clear on what cultural attributes you need to thrive. Not what sounds good in theory, but what you’ve actually needed in roles where you’ve done your best work.</p><p>For me, that includes:</p><p>* Collaborative decision-making where PM insights influence strategy</p><p>* Data-informed but not data-servant culture that values research </p><p>* Psychological safety to propose bold ideas and learn from failures</p><p>* Work-life integration that respects boundaries</p><p>Your list will be different. Maybe you thrive in competitive environments. Maybe you need more structure. Maybe remote work is essential. Be honest with yourself.</p><p><strong>Step 2: Research Before You Apply</strong></p><p>Don’t just apply to every open PM role. Albino recommends something smarter: “Make a list of those companies that you have learned about a little bit about their culture. Maybe you have a friend that worked at a company and they told you that it was an amazing place to work. So make a list of those companies and ask people about their companies they work for.”</p><p>Use LinkedIn to find people who’ve worked at target companies. Look for patterns in how long people stay. Read Glassdoor reviews not for specific complaints but for themes. Check whether executives walk the talk on platforms like Twitter or in company blog posts.</p><p>This front-loaded research saves you from wasting time in processes with companies where you’ll never fit.</p><p><strong>Step 3: Interview Your Interviewers</strong></p><p>During the interview process, systematically assess culture through:</p><p>* How they respond to your questions (defensive vs. open)</p><p>* Whether they can articulate values with specific examples</p><p>* How they talk about past failures and learning</p><p>* Whether individual contributors speak freely or defer to managers</p><p>* How they describe decision-making processes</p><p>* What they emphasize in describing the role (impact vs. tasks)</p><p><strong>Step 4: Talk to Your Future Boss</strong></p><p>Albino is adamant about this: “What’s really important is to get to talk to the hiring manager. Usually if you get to the final stages you get to talk, but if they are not planning on doing that, that’s critical because people join organizations because of the company and they leave the organization most likely because of the boss.”</p><p>Don’t accept an offer without substantive conversation with your direct manager. If the company won’t arrange it, that tells you something about the culture. I’d argue talking to the skip level is also really important if available.</p><p><strong>Step 5: Trust Your Gut, But Verify</strong></p><p>Pay attention to how you feel during the process. Are you energized or drained? Do you find yourself trying to be someone you’re not? Do the people you meet seem genuinely engaged or going through the motions?</p><p>But don’t rely only on feelings. Look for concrete evidence. Ask for examples. Request to speak with current team members. If they’re not willing to arrange it, that’s a red flag.</p><p>Choosing Culture Over Brand</p><p>One of Albino’s most powerful points challenges the default Silicon Valley career path: “You need to be intentional. You need to be really clear on what you want in your next job and not just go for the brand, not just go for the open position. Look for the environment, the leadership, and ask people that have worked there.”</p><p>This is hard advice to follow. The brand matters. The comp matters. The resume line matters.</p><p>But I’ve watched too many talented PMs burn out, get fired, or quietly quit because they optimized for the wrong variables. They went for the FAANG or unicorn prestige without assessing whether they could actually thrive there. They took the higher offer without asking about the leadership style. They joined the hot startup without understanding the culture they were stepping into.</p><p>The intentional career path looks different:</p><p>* Define success for yourself (not what TechCrunch or your parents think success looks like)</p><p>* Identify companies whose cultures align with your needs</p><p>* Pursue those companies specifically, even if they don’t have posted openings</p><p>* Assess fit rigorously during the interview process</p><p>* Choose the role where you can do your best work, even if it’s not the highest offer</p><p>This approach requires confidence. It requires clarity. It requires believing that your best work in the right culture is worth more than mediocre work in a prestigious culture.</p><p>What This Means for Your Next Career Move</p><p>If you’re currently employed and happy, use this framework to understand WHY you’re happy. What cultural attributes are enabling your success? How can you protect and expand them?</p><p>If you’re currently employed and miserable, stop trying to fix yourself. The problem might not be you, it might be cultural misalignment. Start researching cultures where your strengths would be assets, not liabilities.</p><p>If you’re searching for your next role, resist the temptation to spray and pray. Be intentional. Research culture. Ask hard questions. Be authentic in the process. The goal isn’t to get the most offers. The goal is to get the right offer.</p><p>And if you’re a hiring manager or product leader, recognize that culture isn’t something HR handles. Culture is shaped by your leadership every single day. The questions you ask, the behaviors you model, the decisions you make—these create the environment where your team either thrives or survives.</p><p>The Future of Culture and Product Management</p><p>Albino’s work on AI-powered culture assessment points to something bigger: culture is becoming quantifiable. We’re moving from vague values statements to measured behaviors. From gut-feel assessments to systematic evaluation.</p><p>For PMs, this is good news. It means you can make more informed decisions. It means companies can be more honest about their cultures instead of pretending to be something they’re not. It means better matches, longer tenure, and more impact.</p><p>But it also means you need to get serious about understanding culture. It’s no longer enough to read the values page on the careers site and hope for the best.</p><p>You need to research. You need to ask questions. You need to assess fit as rigorously as the company assesses your product skills.</p><p>Your Next Steps</p><p>Here’s what I’m taking away from my conversation with Albino, and what I recommend you do too:</p><p><strong>This Week:</strong></p><p>* Write down the cultural attributes of every job you’ve had where you thrived</p><p>* Identify patterns: what conditions enable your best work?</p><p>* Make a list of companies you’ve heard have cultures aligned with your needs</p><p><strong>This Month:</strong></p><p>* Reach out to three people who work at companies on your list</p><p>* Ask them specific questions about leadership, decision-making, and day-to-day culture</p><p>* Update your interview preparation to include questions that assess culture</p><p><strong>This Quarter:</strong></p><p>* If you’re searching, be more selective about where you apply</p><p>* If you’re employed, have an honest conversation with your manager about cultural alignment</p><p>* If you’re a leader, audit your own behaviors—are you creating the culture you claim to value?</p><p>Culture isn’t soft. Culture isn’t secondary. Culture is the environment where your product skills either flourish or wither.</p><p>Choose wisely.</p><p>If you’re navigating a career transition or want to develop a more intentional approach to your product leadership journey, I offer 1:1 executive, career, and product coaching. Learn more at <a target="_blank" href="https://tomleungcoaching.com">tomleungcoaching.com</a>.</p><p>And if you’re interested in being a beta tester for Albino’s culture fit assessment tool, reach out to him at <a target="_blank" href="mailto:albino@ahaimpact.com">albino@ahaimpact.com</a> or visit <a target="_blank" href="https://ahaimpact.com">ahaimpact.com</a>. He’s looking for a few more organizations to participate in January testing at a significantly discounted rate.</p><p>OK. Let’s ship greatness.</p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://firesidepm.substack.com?utm_medium=podcast&utm_campaign=CTA_1">firesidepm.substack.com</a>
December 15, 2025
<p>TLDR: It was Claude :-)When I set out to compare ChatGPT, Claude, Gemini, Grok, and ChatPRD for writing Product Requirement Documents, I figured they’d all be roughly equivalent. Maybe some subtle variations in tone or structure, but nothing earth-shattering. They’re all built on similar transformer architectures, trained on massive datasets, and marketed as capable of handling complex business writing.</p><p>What I discovered over 45 minutes of hands-on testing revealed not just which tools are better for PRD creation, but why they’re better, and more importantly, how you should actually be using AI to accelerate your product work without sacrificing quality or strategic thinking.</p><p>If you’re an early or mid-career PM in Silicon Valley, this matters to you. Because here’s the uncomfortable truth: your peers are already using AI to write PRDs, analyze features, and generate documentation. The question isn’t whether to use these tools. The question is whether you’re using the right ones most effectively.</p><p>So let me walk you through exactly what I did, what I learned, and what you should do differently.</p><p>The Setup: A Real-World Test Case</p><p>Here’s how I structured the experiment. As I said at the beginning of my recording, “We are back in the Fireside PM podcast and I did that review of the ChatGPT browser and people seemed to like it and then I asked, uh, in a poll, I think it was a LinkedIn poll maybe, what should my next PM product review be? And, people asked for ChatPRD.”</p><p>So I had my marching orders from the audience. But I wanted to make this more comprehensive than just testing ChatPRD in isolation. I opened up five tabs: ChatGPT, Claude, Gemini, Grok, and ChatPRD.</p><p>For the test case, I chose something realistic and relevant: an AI-powered tutor for high school students. Think KhanAmigo or similar edtech platforms. This gave me a concrete product scenario that’s complex enough to stress-test these tools but straightforward enough that I could iterate quickly.</p><p>But here’s the critical part that too many PMs get wrong when they start using AI for product work: I didn’t just throw a single sentence at these tools and expect magic.</p><p>The “Back of the Napkin” Approach: Why You Still Need to Think</p><p>“I presume everybody agrees that you should have some formulated thinking before you dump it into the chatbot for your PRD,” I noted early in my experiment. “I suppose in the future maybe you could just do, like, a one-sentence prompt and come out with the perfect PRD because it would just know everything about you and your company in the context, but for now we’re gonna do this more, a little old-school AI approach where we’re gonna do some original human thinking.”</p><p>This is crucial. I see so many PMs, especially those newer to the field, treat AI like a magic oracle. They type in “Write me a PRD for a social feature” and then wonder why the output is generic, unfocused, and useless.</p><p>Your job as a PM isn’t to become obsolete. It’s to become more effective. And that means doing the strategic thinking work that AI cannot do for you.</p><p>So I started in Google Docs with what I call a “back of the napkin” PRD structure. Here’s what I included:</p><p><strong>Why:</strong> The strategic rationale. In this case: “Want to complement our existing edtech business with a personalized AI tutor, uh, want to maintain position industry, and grow through innovation. on mission for learners.”</p><p><strong>Target User:</strong> Who are we building for? “High school students interested in improving their grades and fundamentals. Fundamental knowledge topics. Specifically science and math. Students who are not in the top ten percent, nor in the bottom ten percent.”</p><p>This is key—I got specific. Not just “students,” but students in the middle 80%. Not just “any subject,” but science and math. This specificity is what separates useful AI output from garbage.</p><p><strong>Problem to Solve:</strong> What’s broken? “Students want better grades. Students are impatient. Students currently use AI just for finding the answers and less to, uh, understand concepts and practice using them.”</p><p><strong>Key Elements:</strong> The feature set and approach.</p><p><strong>Success Metrics:</strong> How we’d measure success.</p><p>Now, was this a perfectly polished PRD outline? Hell no. As you can see from my transcript, I was literally thinking out loud, making typos, restructuring on the fly. But that’s exactly the point. I put in maybe 10-15 minutes of human strategic thinking. That’s all it took to create a foundation that would dramatically improve what came out of the AI tools.</p><p>Round One: Generating the Full PRD</p><p>With my back-of-the-napkin outline ready, I copied it into each tool with a simple prompt asking them to expand it into a more complete PRD.</p><p>ChatGPT: The Reliable Generalist</p><p>ChatGPT gave me something that was... fine. Competent. Professional. But also deeply uninspiring.</p><p>The document it produced checked all the boxes. It had the sections you’d expect. The writing was clear. But when I read it, I couldn’t shake the feeling that I was reading something that could have been written for literally any product in any company. It felt like “an average of everything out there,” as I noted in my evaluation.</p><p>Here’s what ChatGPT did well: It understood the basic structure of a PRD. It generated appropriate sections. The grammar and formatting were clean. If you needed to hand something in by EOD and had literally no time for refinement, ChatGPT would save you from complete embarrassment.</p><p>But here’s what it lacked: Depth. Nuance. Strategic thinking that felt connected to real product decisions. When it described the target user, it used phrases that could apply to any edtech product. When it outlined success metrics, they were the obvious ones (engagement, retention, test scores) without any interesting thinking about leading indicators or proxy metrics.</p><p>The problem with generic output isn’t that it’s wrong, it’s that it’s invisible. When you’re trying to get buy-in from leadership or alignment from engineering, you need your PRD to feel specific, considered, and connected to your company’s actual strategy. ChatGPT’s output felt like it was written by someone who’d read a lot of PRDs but never actually shipped a product.</p><p>One specific example: When I asked for success metrics, ChatGPT gave me “Student engagement rate, Time spent on platform, Test score improvement.” These aren’t wrong, but they’re lazy. They don’t show any thinking about what specifically matters for an AI tutor versus any other educational product. Compare that to Claude’s output, which got more specific about things like “concept mastery rate” and “question-to-understanding ratio.”</p><p><strong>Actionable Insight:</strong> Use ChatGPT when you need fast, serviceable documentation that doesn’t need to be exceptional. Think: internal updates, status reports, routine communications. Don’t rely on it for strategic documents where differentiation matters. If you do use ChatGPT for important documents, treat its output as a starting point that needs significant human refinement to add strategic depth and company-specific context.</p><p>Gemini: Better Than Expected</p><p>Google’s Gemini actually impressed me more than I anticipated. The structure was solid, and it had a nice balance of detail without being overwhelming.</p><p>What Gemini got right: The writing had a nice flow to it. The document felt organized and logical. It did a better job than ChatGPT at providing specific examples and thinking through edge cases. For instance, when describing the target user, it went beyond demographics to consider behavioral characteristics and motivations.</p><p>Gemini also showed some interesting strategic thinking. It considered competitive positioning more thoughtfully than ChatGPT and proposed some differentiation angles that weren’t in my original outline. Good AI tools should add insight, not just regurgitate your input with better formatting.</p><p>But here’s where it fell short: the visual elements. When I asked for mockups, Gemini produced images that looked more like stock photos than actual product designs. They weren’t terrible, but they weren’t compelling either. They had that AI-generated sheen that makes it obvious they came from an image model rather than a designer’s brain.</p><p>For a PRD that you’re going to use internally with a team that already understands the context, Gemini’s output would work well. The text quality is strong enough, and if you’re in the Google ecosystem (Docs, Sheets, Meet, etc.), the integration is seamless. You can paste Gemini’s output directly into Google Docs and continue iterating there.</p><p>But if you need to create something compelling enough to win over skeptics or secure budget, Gemini falls just short. It’s good, but not great. It’s the solid B+ student: reliably competent but rarely exceptional.</p><p><strong>Actionable Insight:</strong> Gemini is a strong choice if you’re working in the Google ecosystem and need good integration with Docs, Sheets, and other Google Workspace tools. The quality is sufficient for most internal documentation needs. It’s particularly good if you’re working with cross-functional partners who are already in Google Workspace. You can share and collaborate on AI-generated drafts without friction. But don’t expect visual mockups that will wow anyone, and plan to add your own strategic polish for high-stakes documents.</p><p>Grok: Not Ready for Prime Time</p><p>Let’s just say my expectations were low, and Grok still managed to underdeliver. The PRD felt thin, generic, and lacked the depth you need for real product work.</p><p>“I don’t have high expectations for grok, unfortunately,” I said before testing it. Spoiler alert: my low expectations were validated.</p><p><strong>Actionable Insight:</strong> Skip Grok for product documentation work right now. Maybe it’ll improve, but as of my testing, it’s simply not competitive with the other options. It felt like 1-2 years behind the others.</p><p>ChatPRD: The Specialized Tool</p><p>Now this was interesting. ChatPRD is purpose-built for PRDs, using foundational models underneath but with specific tuning and structure for product documentation.</p><p>The result? The structure was logical, the depth was appropriate, and it included elements that showed understanding of what actually matters in a PRD. As I reflected: “Cause this one feels like, A human wrote this PRD.”</p><p>The interface guides you through the process more deliberately than just dumping text into a general chat interface. It asks clarifying questions. It structures the output more thoughtfully.</p><p><strong>Actionable Insight:</strong> If you’re a technical lead without a dedicated PM, or you’re a PM who wants a more structured approach to using AI for PRDs, ChatPRD is worth the specialized focus. It’s particularly good when you need something that feels authentic enough to share with stakeholders without heavy editing.</p><p>Claude: The Clear Winner</p><p>But the standout performer, and I’m ranking these, was Claude.</p><p>“I think we know that for now, I’m gonna say Claude did the best job,” I concluded after all the testing. Claude produced the most comprehensive, thoughtful, and strategically sound PRD. But what really set it apart were the concept mocks.</p><p>When I asked each tool to generate visual mockups of the product, Claude produced HTML prototypes that, while not fully functional, looked genuinely compelling. They had thoughtful UI design, clear information architecture, and felt like something that could actually guide development.</p><p>“They were, like, closer to, like, what a Lovable would produce or something like that,” I noted, referring to the quality of low-fidelity prototypes that good designers create.</p><p>The text quality was also superior: more nuanced, better structured, and with more strategic depth. It felt like Claude understood not just what a PRD should contain, but why it should contain those elements.</p><p><strong>Actionable Insight:</strong> For any PRD that matters, meaning anything you’ll share with leadership, use to get buy-in, or guide actual product development, you might as well start with Claude. The quality difference is significant enough that it’s worth using Claude even if you primarily use another tool for other tasks.</p><p>Final Rankings: The Definitive Hierarchy</p><p>After testing all five tools on multiple dimensions: initial PRD generation, visual mockups, and even crafting a pitch paragraph for a skeptical VP of Engineering, here’s my final ranking:</p><p>* <strong>Claude</strong> - Best overall quality, most compelling mockups, strongest strategic thinking</p><p>* <strong>ChatPRD</strong> - Best for structured PRD creation, feels most “human”</p><p>* <strong>Gemini</strong> - Solid all-around performance, good Google integration</p><p>* <strong>ChatGPT</strong> - Reliable but generic, lacks differentiation</p><p>* <strong>Grok</strong> - Not competitive for this use case</p><p>“I’d probably say Claude, then chat PRD, then Gemini, then chat GPT, and then Grock,” I concluded.</p><p>The Deeper Lesson: Garbage In, Garbage Out (Still Applies)</p><p>But here’s what matters more than which tool wins: the realization that hit me partway through this experiment.</p><p>“I think it really does come down to, like, you know, the quality of the prompt,” I observed. “So if our prompt were a little more detailed, all that were more thought-through, then I’m sure the output would have been better. But as you can see we didn’t really put in brain trust prompting here. Just a little bit of, kind of hand-wavy prompting, but a little better than just one or two sentences.”</p><p>And we still got pretty good results.</p><p>This is the meta-insight that should change how you approach AI tools in your product work: <strong>The quality of your input determines the quality of your output, but the baseline quality of the tool determines the ceiling of what’s possible.</strong></p><p>No amount of great prompting will make Grok produce Claude-level output. But even mediocre prompting with Claude will beat great prompting with lesser tools.</p><p>So the dual strategy is:</p><p>* Use the best tool available (currently Claude for PRDs)</p><p>* Invest in improving your prompting skills ideally with as much original and insightful human, company aware, and context aware thinking as possible.</p><p>Real-World Workflows: How to Actually Use This in Your Day-to-Day PM Work</p><p>Theory is great. Here’s how to incorporate these insights into your actual product management workflows.</p><p>The Weekly Sprint Planning Workflow</p><p>Every PM I know spends hours each week preparing for sprint planning. You need to refine user stories, clarify acceptance criteria, anticipate engineering questions, and align with design and data science. AI can compress this work significantly.</p><p><strong>Here’s an example workflow:</strong></p><p><strong>Monday morning (30 minutes):</strong></p><p>* Review upcoming priorities and open your rough notes/outline in Google Docs</p><p>* Open Claude and paste your outline with this prompt:</p><p>“I’m preparing for sprint planning. Based on these priorities [paste notes], generate detailed user stories with acceptance criteria. Format each as: User story, Business context, Technical considerations, Acceptance criteria, Dependencies, Open questions.”</p><p><strong>Monday afternoon (20 minutes):</strong></p><p>* Review Claude’s output critically</p><p>* Identify gaps, unclear requirements, or missing context</p><p>* Follow up with targeted prompts:</p><p>“The user story about authentication is too vague. Break it down into separate stories for: social login, email/password, session management, and password reset. For each, specify security requirements and edge cases.”</p><p><strong>Tuesday morning (15 minutes):</strong></p><p>* Generate mockups for any UI-heavy stories:</p><p>“Create an HTML mockup for the login flow showing: landing page, social login options, email/password form, error states, and success redirect.”</p><p>* Even if the HTML doesn’t work perfectly, it gives your designers a starting point</p><p><strong>Before sprint planning (10 minutes):</strong></p><p>* Ask Claude to anticipate engineering questions:</p><p>“Review these user stories as if you’re a senior engineer. What questions would you ask? What concerns would you raise about technical feasibility, dependencies, or edge cases?”</p><p>* This preparation makes you look thoughtful and helps the meeting run smoothly</p><p>Total time investment: ~75 minutes. Typical time saved: 3-4 hours compared to doing this manually.</p><p>The Stakeholder Alignment Workflow</p><p>Getting alignment from multiple stakeholders (product leadership, engineering, design, data science, legal, marketing) is one of the hardest parts of PM work. AI can help you think through different stakeholder perspectives and craft compelling communications for each.</p><p><strong>Here’s how:</strong></p><p><strong>Step 1: Map your stakeholders (10 minutes)</strong></p><p>Create a quick table in a doc:</p><p>Stakeholder | Primary Concern | Decision Criteria | Likely Objections VP Product | Strategic fit, ROI | Company OKRs, market opportunity | Resource allocation vs other priorities VP Eng | Technical risk, capacity | Engineering capacity, tech debt | Complexity, unclear requirements Design Lead | User experience | User research, design principles | Timeline doesn’t allow proper design process Legal | Compliance, risk | Regulatory requirements | Data privacy, user consent flows</p><p><strong>Step 2: Generate stakeholder-specific communications (20 minutes)</strong></p><p>For each key stakeholder, ask Claude:</p><p>“I need to pitch this product idea to [Stakeholder]. Based on this PRD, create a 1-page brief addressing their primary concern of [concern from your table]. Open with the specific value for them, address their likely objection of [objection], and close with a clear ask. Tone should be [professional/technical/strategic] based on their role.”</p><p>Then you’ll have customized one-pagers for your pre-meetings with each stakeholder, dramatically increasing your alignment rate.</p><p><strong>Step 3: Synthesize feedback (15 minutes)</strong></p><p>After gathering stakeholder input, ask Claude to help you synthesize:</p><p>“I got the following feedback from stakeholders: [paste feedback]. Identify: (1) Common themes, (2) Conflicting requirements, (3) Legitimate concerns vs organizational politics, (4) Recommended compromises that might satisfy multiple parties.”</p><p>This pattern-matching across stakeholder feedback is something AI does really well and saves you hours of mental processing.</p><p>The Quarterly Planning Workflow</p><p>Quarterly or annual planning is where product strategy gets real. You need to synthesize market trends, customer feedback, technical capabilities, and business objectives into a coherent roadmap. AI can accelerate this dramatically.</p><p><strong>Six weeks before planning:</strong></p><p>* Start collecting input (customer interviews, market research, competitive analysis, engineering feedback)</p><p>* Don’t wait until the last minute</p><p><strong>Four weeks before planning:</strong></p><p>Dump everything into Claude with this structure:</p><p>“I’m creating our Q2 roadmap. Context:</p><p>* Business objectives: [paste from leadership]</p><p>* Customer feedback themes: [paste synthesis]</p><p>* Technical capabilities/constraints: [paste from engineering]</p><p>* Competitive landscape: [paste analysis]</p><p>* Current product gaps: [paste from your analysis]</p><p>Generate 5 strategic themes that could anchor our Q2 roadmap. For each theme:</p><p>* Strategic rationale (how it connects to business objectives)</p><p>* Key initiatives (2-3 major features/projects)</p><p>* Success metrics</p><p>* Resource requirements (rough estimate)</p><p>* Risks and mitigations</p><p>* Customer segments addressed”</p><p>This gives you a strategic framework to react to rather than starting from a blank page.</p><p><strong>Three weeks before planning:</strong></p><p>Iterate on the most promising themes:</p><p>“Deep dive on Theme 3. Generate:</p><p>* Detailed initiative breakdown</p><p>* Dependencies on platform/infrastructure</p><p>* Phasing options (MVP vs full build)</p><p>* Go-to-market considerations</p><p>* Data requirements</p><p>* Open questions requiring research”</p><p><strong>Two weeks before planning:</strong></p><p>Pressure-test your thinking:</p><p>“Play devil’s advocate on this roadmap. What are the strongest arguments against each initiative? What am I likely missing? What failure modes should I plan for?”</p><p>This adversarial prompting forces you to strengthen weak points before your leadership reviews it.</p><p><strong>One week before planning:</strong></p><p>Generate your presentation:</p><p>“Create an executive presentation for this roadmap. Structure: (1) Market context and strategic imperative, (2) Q2 themes and initiatives, (3) Expected outcomes and metrics, (4) Resource requirements, (5) Key risks and mitigations, (6) Success criteria for decision. Make it compelling but data-driven. Tone: confident but not overselling.”</p><p>Then add your company-specific context, visual brand, and personal voice.</p><p>The Customer Research Workflow</p><p>AI can’t replace talking to customers, but it can help you prepare better questions, analyze feedback more systematically, and identify patterns faster.</p><p><strong>Before customer interviews:</strong></p><p>“I’m interviewing customers about [topic]. Generate:</p><p>* 10 open-ended questions that avoid leading the witness</p><p>* 5 follow-up questions for each main question</p><p>* Common cognitive biases I should watch for</p><p>* A framework for categorizing responses”</p><p>This prep work helps you conduct better interviews.</p><p><strong>After interviews:</strong></p><p>“I conducted 15 customer interviews. Here are the key quotes: [paste anonymized quotes]. Identify:</p><p>* Recurring themes and patterns</p><p>* Surprising insights that contradict our assumptions</p><p>* Segments with different needs</p><p>* Implied needs customers didn’t articulate directly</p><p>* Recommended next steps for validation”</p><p>AI is excellent at pattern-matching across qualitative data at scale.</p><p>The Crisis Management Workflow</p><p>Something broke. The site is down. Data was lost. A feature shipped with a critical bug. You need to move fast.</p><p><strong>Immediate response (5 minutes):</strong></p><p>“Critical incident. Details: [brief description]. Generate:</p><p>* Incident classification (Sev 1-4)</p><p>* Immediate stakeholders to notify</p><p>* Draft customer communication (honest, apologetic, specific about what happened and what we’re doing)</p><p>* Draft internal communication for leadership</p><p>* Key questions to ask engineering during investigation”</p><p>Having these drafted in 5 minutes lets you focus on coordination and decision-making rather than wordsmithing.</p><p><strong>Post-incident (30 minutes):</strong></p><p>“Write a post-mortem based on this incident timeline: [paste timeline]. Include:</p><p>* What happened (technical details)</p><p>* Root cause analysis</p><p>* Impact quantification (users affected, revenue impact, time to resolution)</p><p>* What went well in our response</p><p>* What could have been better</p><p>* Specific action items with owners and deadlines</p><p>* Process changes to prevent recurrence Tone: Blameless, focused on learning and improvement.”</p><p>This gives you a strong first draft to refine with your team.</p><p>Common Pitfalls: What Not to Do with AI in Product Management</p><p>Now let’s talk about the mistakes I see PMs making with AI tools. </p><p>Pitfall #1: Treating AI Output as Final</p><p>The biggest mistake is copy-pasting AI output directly into your PRD, roadmap presentation, or stakeholder email without critical review.</p><p>The result? Documents that are grammatically perfect but strategically shallow. Presentations that sound impressive but don’t hold up under questioning. Emails that are professionally worded but miss the subtext of organizational politics.</p><p><strong>The fix:</strong> Always ask yourself:</p><p>* Does this reflect my actual strategic thinking, or generic best practices?</p><p>* Would my CEO/engineering lead/biggest customer find this compelling and specific?</p><p>* Are there company-specific details, customer insights, or technical constraints that only I know?</p><p>* Does this sound like me, or like a robot?</p><p>Add those elements. That’s where your value as a PM comes through.</p><p>Pitfall #2: Using AI as a Crutch Instead of a Tool</p><p>Some PMs use AI because they don’t want to think deeply about the product. They’re looking for AI to do the hard work of strategy, prioritization, and trade-off analysis.</p><p>This never works. AI can help you think more systematically, but it can’t replace thinking.</p><p>If you find yourself using AI to avoid wrestling with hard questions (”Should we build X or Y?” “What’s our actual competitive advantage?” “Why would customers switch from the incumbent?”), you’re using it wrong.</p><p><strong>The fix:</strong> Use AI to explore options, not to make decisions. Generate three alternatives, pressure-test each one, then use your judgment to decide. The AI can help you think through implications, but you’re still the one choosing.</p><p>Pitfall #3: Not Iterating</p><p>Getting mediocre AI output and just accepting it is a waste of the technology’s potential.</p><p>The PMs who get exceptional results from AI are the ones who iterate. They generate an initial response, identify what’s weak or missing, and ask follow-up questions. They might go through 5-10 iterations on a key section of a PRD.</p><p>Each iteration is quick (30 seconds to type a follow-up prompt, 30 seconds to read the response), but the cumulative effect is dramatically better output.</p><p><strong>The fix:</strong> Budget time for iteration. Don’t try to generate a complete, polished PRD in one prompt. Instead, generate a rough draft, then spend 30 minutes iterating on specific sections that matter most.</p><p>Pitfall #4: Ignoring the Political and Human Context</p><p>AI tools have no understanding of organizational politics, interpersonal relationships, or the specific humans you’re working with.</p><p>They don’t know that your VP of Engineering is burned out and skeptical of any new initiatives. They don’t know that your CEO has a personal obsession with a specific competitor. They don’t know that your lead designer is sensitive about not being included early enough in the process.</p><p>If you use AI-generated communications without layering in this human context, you’ll create perfectly worded documents that land badly because they miss the subtext.</p><p><strong>The fix:</strong> After generating AI content, explicitly ask yourself: “What human context am I missing? What relationships do I need to consider? What political dynamics are in play?” Then modify the AI output accordingly.</p><p>Pitfall #5: Over-Relying on a Single Tool</p><p>Different AI tools have different strengths. Claude is great for strategic depth, ChatPRD is great for structure, Gemini integrates well with Google Workspace.</p><p>If you only ever use one tool, you’re missing opportunities to leverage different strengths for different tasks.</p><p><strong>The fix:</strong> Keep 2-3 tools in your toolkit. Use Claude for important PRDs and strategic documents. Use Gemini for quick internal documentation that needs to integrate with Google Docs. Use ChatPRD when you want more guided structure. Match the tool to the task.</p><p>Pitfall #6: Not Fact-Checking AI Output</p><p>AI tools hallucinate. They make up statistics, misrepresent competitors, and confidently state things that aren’t true. If you include those hallucinations in a PRD that goes to leadership, you look incompetent.</p><p><strong>The fix:</strong> Fact-check everything, especially:</p><p>* Statistics and market data</p><p>* Competitive feature claims</p><p>* Technical capabilities and limitations</p><p>* Regulatory and compliance requirements</p><p>If the AI cites a number or makes a factual claim, verify it independently before including it in your document.</p><p>The Meta-Skill: Prompt Engineering for PMs</p><p>Let’s zoom out and talk about the underlying skill that makes all of this work: prompt engineering.</p><p>This is a real skill. The difference between a mediocre prompt and a great prompt can be 10x difference in output quality. And unlike coding or design, where there’s a steep learning curve, prompt engineering is something you can get good at quickly.</p><p><strong>Principle 1: Provide Context Before Instructions</strong></p><p>Bad prompt:</p><p>“Write a PRD for an AI tutor”</p><p>Good prompt:</p><p>“I’m a PM at an edtech company with 2M users, primarily high school students. We’re exploring an AI tutor feature to complement our existing video content library and practice problems. Our main competitors are Khan Academy and Course Hero. Our differentiation is personalized learning paths based on student performance data.</p><p>Write a PRD for an AI tutor feature targeting students in the middle 80% academically who struggle with science and math.”</p><p>The second prompt gives Claude the context it needs to generate something specific and strategic rather than generic.</p><p><strong>Principle 2: Specify Format and Constraints</strong></p><p>Bad prompt:</p><p>“Generate success metrics”</p><p>Good prompt:</p><p>“Generate 5-7 success metrics for this feature. Include a mix of:</p><p>* Leading indicators (early signals of success)</p><p>* Lagging indicators (definitive success measures)</p><p>* User behavior metrics</p><p>* Business impact metrics</p><p>For each metric, specify: name, definition, target value, measurement method, and why it matters.”</p><p>The structure you provide shapes the structure you get back.</p><p><strong>Principle 3: Ask for Multiple Options</strong></p><p>Bad prompt:</p><p>“What should our Q2 priorities be?”</p><p>Good prompt:</p><p>“Generate 3 different strategic approaches for Q2:</p><p>* Option A: Focus on user acquisition</p><p>* Option B: Focus on engagement and retention</p><p>* Option C: Focus on monetization</p><p>For each option, detail: key initiatives, expected outcomes, resource requirements, risks, and recommendation for or against.”</p><p>Asking for multiple options forces the AI (and forces you) to think through trade-offs systematically.</p><p><strong>Principle 4: Specify Audience and Tone</strong></p><p>Bad prompt:</p><p>“Summarize this PRD”</p><p>Good prompt:</p><p>“Create a 1-paragraph summary of this PRD for our skeptical VP of Engineering. Tone: Technical, concise, addresses engineering concerns upfront. Focus on: technical architecture, resource requirements, risks, and expected engineering effort. Avoid marketing language.”</p><p>The audience and tone specification ensures the output will actually work for your intended use.</p><p><strong>Principle 5: Use Iterative Refinement</strong></p><p>Don’t try to get perfect output in one prompt. Instead:</p><p>First prompt: Generate rough draft Second prompt: “This is too generic. Add specific examples from [our company context].” Third prompt: “The technical section is weak. Expand with architecture details and dependencies.” Fourth prompt: “Good. Now make it 30% more concise while keeping the key details.”</p><p>Each iteration improves the output incrementally.</p><p>Let me break down the prompting approach that worked in this experiment, because this is immediately actionable for your work tomorrow.</p><p>Strategy 1: The Structured Outline Approach</p><p>Don’t go from zero to full PRD in one prompt. Instead:</p><p>* <strong>Start with strategic thinking</strong> - Spend 10-15 minutes outlining why you’re building this, who it’s for, and what problem it solves</p><p>* <strong>Get specific</strong> - Don’t say “users,” say “high school students in the middle 80% of academic performance”</p><p>* <strong>Include constraints</strong> - Budget, timeline, technical limitations, competitive landscape</p><p>* <strong>Dump your outline into the AI</strong> - Now ask it to expand into a full PRD</p><p>* <strong>Iterate section by section</strong> - Don’t try to perfect everything at once</p><p>This is exactly what I did in my experiment, and even with my somewhat sloppy outline, the results were dramatically better than they would have been with a single-sentence prompt.</p><p>Strategy 2: The Comparative Analysis Pattern</p><p>One technique I used that worked particularly well: asking each tool to do the same specific task and comparing results.</p><p>For example, I asked all five tools: “Please compose a one paragraph exact summary I can share over DM with a highly influential VP of engineering who is generally a skeptic but super smart.”</p><p>This forced each tool to synthesize the entire PRD into a compelling pitch while accounting for a specific, challenging audience. The variation in quality was revealing—and it gave me multiple options to choose from or blend together.</p><p><strong>Actionable tip:</strong> When you need something critical (a pitch, an executive summary, a key decision framework), generate it with 2-3 different AI tools and take the best elements from each. This “ensemble approach” often produces better results than any single tool.</p><p>Strategy 3: The Iterative Refinement Loop</p><p>Don’t treat the AI output as final. Use it as a first draft that you then refine through conversation with the AI.</p><p>After getting the initial PRD, I could have asked follow-up questions like:</p><p>* “What’s missing from this PRD?”</p><p>* “How would you strengthen the success metrics section?”</p><p>* “Generate 3 alternative approaches to the core feature set”</p><p>Each iteration improves the output and, more importantly, forces me to think more deeply about the product.</p><p>What This Means for Your Career</p><p>If you’re an early or mid-career PM reading this, you might be thinking: “Great, so AI can write PRDs now. Am I becoming obsolete?”</p><p>Absolutely not. But your role is evolving, and understanding that evolution is critical.</p><p>The PMs who will thrive in the AI era are those who:</p><p>* <strong>Excel at strategic thinking</strong> - AI can generate options, but you need to know which options align with company strategy, customer needs, and technical feasibility</p><p>* <strong>Master the art of prompting</strong> - This is a genuine skill that separates mediocre AI users from exceptional ones</p><p>* <strong>Know when to use AI and when not to</strong> - Some aspects of product work benefit enormously from AI. Others (user interviews, stakeholder negotiation, cross-functional relationship building) require human judgment and empathy</p><p>* <strong>Can evaluate AI output critically</strong> - You need to spot the hallucinations, the generic fluff, and the strategic misalignments that AI inevitably produces</p><p>Think of AI tools as incredibly capable interns. They can produce impressive work quickly, but they need direction, oversight, and strategic guidance. Your job is to provide that guidance while leveraging their speed and breadth.</p><p>The Real-World Application: What to Do Monday Morning</p><p>Let’s get tactical. Here’s exactly how to apply these insights to your actual product work:</p><p>For Your Next PRD:</p><p>* <strong>Block 30 minutes for strategic thinking</strong> - Write your back-of-the-napkin outline in Google Docs or your tool of choice</p><p>* <strong>Open Claude</strong> (or ChatPRD if you want more structure)</p><p>* <strong>Copy your outline with this prompt:</strong></p><p>“I’m a product manager at [company] working on [product area]. I need to create a comprehensive PRD based on this outline. Please expand this into a complete PRD with the following sections: [list your preferred sections]. Make it detailed enough for engineering to start breaking down into user stories, but concise enough for leadership to read in 15 minutes. [Paste your outline]”</p><p>* <strong>Review the output critically</strong> - Look for generic statements, missing details, or strategic misalignments</p><p>* <strong>Iterate on specific sections:</strong></p><p>“The success metrics section is too vague. Please provide 3-5 specific, measurable KPIs with target values and explanation of why these metrics matter.”</p><p>* <strong>Generate supporting materials:</strong></p><p>“Create a visual mockup of the core user flow showing the key interaction points.”</p><p>* <strong>Synthesize the best elements</strong> - Don’t just copy-paste the AI output. Use it as raw material that you shape into your final document</p><p>For Stakeholder Communication:</p><p>When you need to pitch something to leadership or engineering:</p><p>* <strong>Generate 3 versions</strong> of your pitch using different tools (Claude, ChatPRD, and one other)</p><p>* <strong>Compare them for:</strong></p><p>* Clarity and conciseness</p><p>* Strategic framing</p><p>* Compelling value proposition</p><p>* Addressing likely objections</p><p>* <strong>Blend the best elements</strong> into your final version</p><p>* <strong>Add your personal voice</strong> - This is crucial. AI output often lacks personality and specific company context. Add that yourself.</p><p>For Feature Prioritization:</p><p>AI tools can help you think through trade-offs more systematically:</p><p>“I’m deciding between three features for our next release: [Feature A], [Feature B], and [Feature C]. For each feature, analyze: (1) Estimated engineering effort, (2) Expected user impact, (3) Strategic alignment with making our platform the go-to solution for [your market], (4) Risk factors. Then recommend a prioritization with rationale.”</p><p>This doesn’t replace your judgment, but it forces you to think through each dimension systematically and often surfaces considerations you hadn’t thought of.</p><p>The Uncomfortable Truth About AI and Product Management</p><p>Let me be direct about something that makes many PMs uncomfortable: AI will make some PM skills less valuable while making others more valuable.</p><p><strong>Less valuable:</strong></p><p>* Writing boilerplate documentation</p><p>* Creating standard frameworks and templates</p><p>* Generating routine status updates</p><p>* Synthesizing information from existing sources</p><p><strong>More valuable:</strong></p><p>* Strategic product vision and roadmapping</p><p>* Deep customer empathy and insight generation</p><p>* Cross-functional leadership and influence</p><p>* Critical evaluation of options and trade-offs</p><p>* Creative problem-solving for novel situations</p><p>If your PM role primarily involves the first category of tasks, you should be concerned. But if you’re focused on the second category while leveraging AI for the first, you’re going to be exponentially more effective than your peers who resist these tools.</p><p>The PMs I see succeeding aren’t those who can write the best PRD manually. They’re those who can write the best PRD with AI assistance in one-tenth the time, then use the saved time to talk to more customers, think more deeply about strategy, and build stronger cross-functional relationships.</p><p>Advanced Techniques: Beyond Basic PRD Generation</p><p>Once you’ve mastered the basics, here are some advanced applications I’ve found valuable:</p><p>Competitive Analysis at Scale</p><p>“Research our top 5 competitors in [market]. For each one, analyze: their core value proposition, key features, pricing strategy, target customer, and likely product roadmap based on recent releases and job postings. Create a comparison matrix showing where we have advantages and gaps.”</p><p>Then use web search tools in Claude or Perplexity to fact-check and expand the analysis.</p><p>Scenario Planning</p><p>“We’re considering three strategic directions for our product: [Direction A], [Direction B], [Direction C]. For each direction, map out: likely customer adoption curve, required technical investments, competitive positioning in 12 months, and potential pivots if the hypothesis proves wrong. Then identify the highest-risk assumptions we should test first for each direction.”</p><p>This kind of structured scenario thinking is exactly what AI excels at—generating multiple well-reasoned perspectives quickly.</p><p>User Story Generation</p><p>After your PRD is solid:</p><p>“Based on this PRD, generate a complete set of user stories following the format ‘As a [user type], I want to [action] so that [benefit].’ Include acceptance criteria for each story. Organize them into epics by functional area.”</p><p>This can save your engineering team hours of grooming meetings.</p><p>The Tools Will Keep Evolving. Your Process Shouldn’t</p><p>Here’s something important to remember: by the time you read this, the specific rankings might have shifted. Maybe ChatGPT-5 has leapfrogged Claude. Maybe a new specialized tool has emerged.</p><p>But the core principles won’t change:</p><p>* Do strategic thinking before touching AI</p><p>* Use the best tool available for your specific task</p><p>* Iterate and refine rather than accepting first outputs</p><p>* Blend AI capabilities with human judgment</p><p>* Focus your time on the uniquely human aspects of product management</p><p>The specific tools matter less than your process for using them effectively.</p><p>A Final Experiment: The Skeptical VP Test</p><p>I want to share one more insight from my testing that I think is particularly relevant for early and mid-career PMs.</p><p>Toward the end of my experiment, I gave each tool this prompt: “Please compose a one paragraph exact summary I can share over DM with a highly influential VP of engineering who is generally a skeptic but super smart.”</p><p>This is such a realistic scenario. How many times have you needed to pitch an idea to a skeptical technical leader via Slack or email? Someone who’s brilliant, who’s seen a thousand product ideas fail, and who can spot b******t from a mile away?</p><p>The quality variation in the responses was fascinating. ChatGPT gave me something that felt generic and safe. Gemini was better but still a bit too enthusiastic. Grok was... well, Grok.</p><p>But Claude and ChatPRD both produced messages that felt authentic, technically credible, and appropriately confident without being overselling. They acknowledged the engineering challenges while framing the opportunity compellingly.</p><p><strong>The lesson:</strong> When the stakes are high and the audience is sophisticated, the quality of your AI tool matters even more. That skeptical VP can tell the difference between a carefully crafted message and AI-generated fluff. So can your CEO. So can your biggest customers.</p><p>Use the best tools available, but more importantly, always add your own strategic thinking and authentic voice on top.</p><p>Questions to Consider: A Framework for Your Own Experiments</p><p>As I wrapped up my Loom, I posed some questions to the audience that I’ll pose to you:</p><p>“Let me know in the comments, if you do your PRDs using AI differently, do you start with back of the envelope? Do you say, oh no, I just start with one sentence, and then I let the chatbot refine it with me? Or do you go way more detailed and then use the chatbot to kind of pressure test it?”</p><p>These aren’t rhetorical questions. Your answer reveals your approach to AI-augmented product work, and different approaches work for different people and contexts.</p><p><strong>For early-career PMs:</strong> I’d recommend starting with more detailed outlines. The discipline of thinking through your product strategy before touching AI will make you a stronger PM. You can always compress that process later as you get more experienced.</p><p><strong>For mid-career PMs:</strong> Experiment with different approaches for different types of documents. Maybe you do detailed outlines for major feature PRDs but use more iterative AI-assisted refinement for smaller features or updates. Find what optimizes your personal productivity while maintaining quality.</p><p><strong>For senior PMs and product leaders:</strong> Consider how AI changes what you should expect from your PM team. Should you be reviewing more AI-generated first drafts and spending more time on strategic guidance? Should you be training your team on effective AI usage? These are leadership questions worth grappling with.</p><p>The Path Forward: Continuous Experimentation</p><p>My experiment with these five AI tools took 45 minutes. But I’m not done experimenting.</p><p>The field of AI-assisted product management is evolving rapidly. New tools launch monthly. Existing tools get smarter weekly. Prompting techniques that work today might be obsolete in three months.</p><p>Your job, if you want to stay at the forefront of product management, is to continuously experiment. Try new tools. Share what works with your peers. Build a personal knowledge base of effective prompts and workflows. And be generous with what you learn. The PM community gets stronger when we share insights rather than hoarding them.</p><p>That’s why I created this Loom and why I’m writing this post. Not because I have all the answers, but because I’m figuring it out in real-time and want to share the journey.</p><p>A Personal Note on Coaching and Consulting</p><p>If this kind of practical advice resonates with you, I’m happy to work with you directly.</p><p>Through my pm coaching practice, I offer 1:1 executive, career, and product coaching for PMs and product leaders. We can dig into your specific challenges: whether that’s leveling up your AI workflows, navigating a career transition, or developing your strategic product thinking.</p><p>I also work with companies (usually startups or incubation teams) on product strategy, helping teams figure out PMF for new explorations and improving their product management function.</p><p>The format is flexible. Some clients want ongoing coaching, others prefer project-based consulting, and some just want a strategic sounding board for a specific decision. Whatever works for you.</p><p>Reach out through <a target="_blank" href="http://tomleungcoaching.com">tomleungcoaching.com</a> if you’re interested in working together.</p><p>OK. Enough pontificating. Let’s ship greatness.</p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://firesidepm.substack.com?utm_medium=podcast&utm_campaign=CTA_1">firesidepm.substack.com</a>
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Product Management podcast where 20 year PM veteran Tom Leung interviews VP's, CPO's, and CEO's who rose up from product to talk about their careers, the art and science of product management, and advice for other PM's.
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