The Future of Work: Why Product People Matter

🔥 𝗪𝗵𝗮𝘁 𝗜 𝗕𝗲𝗹𝗶𝗲𝘃𝗲: “𝙑𝙞𝙗𝙚 𝙘𝙤𝙙𝙞𝙣𝙜” 𝙖𝙣𝙙 𝘼𝙄 𝙩𝙤𝙤𝙡𝙞𝙣𝙜 𝙬𝙤𝙣’𝙩 𝙧𝙚𝙥𝙡𝙖𝙘𝙚 𝙋𝙈𝙨 𝙤𝙧 𝙨𝙡𝙤𝙬 𝙙𝙤𝙬𝙣 𝙩𝙝𝙚 𝙙𝙚𝙢𝙖𝙣𝙙 𝙛𝙤𝙧 𝙚𝙣𝙜𝙞𝙣𝙚𝙚𝙧𝙨 — 𝙞𝙩’𝙡𝙡 𝙨𝙪𝙥𝙚𝙧𝙘𝙝𝙖𝙧𝙜𝙚 🚀 𝙗𝙤𝙩𝙝. Roles, skills, etc, will adapt, not disappear! In the short term, we'll see labor disruption, but growth is coming! 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗶𝗻𝗴 𝗦𝗽𝗲𝗲𝗱. Over the last year, we’ve seen vibe coding reshape how PMs can accelerate idea validation and user feedback. Instead of waiting weeks for polished prototypes, PMs can now generate lightweight, expressive prototypes — ones that are interactive enough for real users to react authentically. 𝗙𝗿𝗼𝗺 𝗮 𝗨𝗫 𝗽𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲, 𝘁𝗵𝗶𝘀 𝘀𝗵𝗶𝗳𝘁 𝗶𝘀 𝗵𝘂𝗴𝗲: 👉 PMs can collect nuanced input earlier, before design and engineering resources commit. 👉 Feedback loops collapse from months into days. 👉 UX researchers get richer qualitative data in half the time. 👉 GenAI can consolidate and summarize the feedback. Turning hundreds of transcripts into patterns instantly. 𝗗𝗲𝗺𝗼𝗰𝗿𝗮𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗜𝗱𝗲𝗮 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 ↗️ 𝗩𝗮𝗹𝘂𝗲 𝗗𝗲𝗺𝗮𝗻𝗱. Here’s the twist → this prototyping speed doesn’t make software engineers less essential. It makes them more impactful. When engineers enter the picture, they’re backed by sharper insights, validated concepts, and a clear signal of what users actually want. The result: faster iteration velocity and higher launch confidence. The “vibe-first” era isn’t about democratizing coding — it’s about accelerating creativity across the stack. It's about democratizing idea generation! Every function — PM, Design, UX, and Eng — can operate closer to the speed of 𝙃𝙐𝙈𝘼𝙉 imagination. #ProductManagement #UXDesign #Engineering #VibeCoding #Innovation #FutureOfWork #ProductDevelopment

“Please review the attached design document by Thursday at 3 pm.” “Ugh,” I sighed, staring at the new email. “Another f-ing design document to review. There goes my afternoon!” I opened the document. 20 pages! Even worse than I thought. My team was small but powerful, comprising some of the most talented engineers I had worked with in my career, and they were thorough. If they had a design task to do, they would do it, and sometimes it was an overwhelming amount of detail. As Mike Tyson said, “Everyone has a plan until they get punched in the mouth.” I’ve found the same to be true of a heavy design process. Having an engineer spend hours to comb through the code and provide a detailed plan of how they’re going to implement a feature - and then having their team members or technical leadership spend yet more hours reviewing that plan - can make everyone feel better about their process, but rarely does that design survive contact with real-world implementation. Worse, our design process was killing us. Killing our development pace, killing our developers’ motivation, and killing our budget. Before the dawn of AI, we struggled with what to do about it. Having no design process seemed like a horrible idea: letting 8 or 10 engineers run loose in the code with little to no oversight outside of a code review. Eventually, we compromised on a lightweight process that at least gave us an initial check before implementation. Then came AI. The debate about the benefits and drawbacks of vibe-coding is still raging, but one area where it has been a game-changer is letting us rely on quick prototypes to explore a feature rather than requiring a crazy detailed design. I still work with some incredibly talented people, and putting the right AI tools in their hands and giving them the freedom to try out their ideas in code without the heavy-handed design process has made a world of difference. 🔥 No more hours spent writing documents instead of code. 🔥 No more hours spent reviewing documents that make your eyes want to fall out. 🔥 No more motivation-killing work. I’m not sure if I’ll get to a place where I trust AI-generated code to just go out to production without a review, but giving talented people the right AI tools to release amazing features at an amazing pace? That’s a process I’ll trust every time.

Prompt engineering isn’t just about writing clever instructions. It’s part of a much larger system of decisions that start long before the first token is generated. -> 1. Defining the Use Case Shapes Everything Before touching any prompt, you define the problem. Are you building a summarizer, a creative writer, or an assistant that gives structured outputs? That decision defines: - How much creativity or precision your model needs - Which model architecture fits better (Claude, Titan, Llama, etc.) - How you’ll balance parameters like temperature, top-p, and top-k. In other words, your prompt begins with your purpose. -> 2. Choosing the Right Model Defines the Rules of the Game Large models perform surprisingly well with zero-shot prompting. Smaller ones often need few-shot examples or fine-tuning. That means model selection isn’t just a technical step — it’s a strategic choice that defines how much work your prompts will need to do later. -> 3. The “Endless Micro-Cycle”: Prompt ↔ Evaluate ↔ Adjust This is the heart of being a Prompt Engineer. You design, test, and refine. Again and again. Every iteration is an experiment in: - Phrasing and structure - Configuration parameters (temperature, top-p, top-k, max tokens) - Evaluation of trade-offs between creativity, consistency, and control. That micro-cycle is endless — but it’s also the craft. It’s where good prompts become great systems. Prompt engineering isn’t static; it’s model management in motion. -> 4. Application Integration: From Prompts to Products The last step is deployment and integration — turning experiments into scalable systems. Here, optimization meets orchestration: - Running your best prompt setup efficiently - Managing costs and latency - Embedding LLMs into real applications. That’s when an experiment becomes a product. What I Learned: Generative AI projects aren’t built in one shot. They’re sculpted — through cycles of definition, selection, adaptation, and alignment. And it’s how you orchestrate that loop between prompting and evaluation that defines how powerful your system becomes.

As a PM, AI saved me hours, but prompts made it priceless. I’ve learnt a lot about prompting, and honestly, it’s been one of the most transformative skills for me. In today’s world, using AI as a product manager is no longer optional, it’s essential. It helps you move faster, organize better, analyze smarter, and focus more on what truly matters: strategy and impact. But here’s the truth, using AI isn’t where it ends. Prompting is where the magic really happens. AI is only as powerful as the question you ask. And as a PM, good prompting can change the way you work, from analyzing user feedback and Uncovering key insights, to generating stakeholder-ready PRDs, research questions, and even helping you shape your next prototype or landing page copy. That’s why I use the RACE framework whenever I prompt: • R — Role: Who should AI be? (e.g., “Act as a senior PM…”) • A — Action: What do you want it to do? • C — Context: What details does it need? • E — Execution: What does a great output look like? RACE gets me cleaner PRDs, sharper research questions, and fast, reliable syntheses — the stuff you’d normally pay for in workshops or consulting. So I packaged my best, comprehensive battle-tested RACE prompts into a Google Doc and I’m giving it away for free. No gate, no catch. Grab the prompts + bias-checks + safety tips here: https://bit.ly/4oskCzx #Prompting# #ProductPrompts# #AIProductManager#

We just rolled out a completely overhauled Project Planning flow to Dreambase users. Context is everything for AI reports, and it just got a lot better. While upgrading, we split the context generation into 4 streams so it’s crazy fast, and upgraded the planning model from GPT‑4o to GPT‑5, and the results are significantly better. The Dream Team of Agents divides and conquers to write the AI Requirements ("AIR") docs for your report, acting as Product Owner, Technical PM, Engineering Architect, and UX Writer. That combo is part of our secret sauce (don't tell anyone! 🥷). Literally as we pressed the deploy button, one of our largest customers happened to be testing a massive enterprise-scale dataset in Supabase and let us know the quality of context and reports spiked immediately. It seems like the industry finally figured out that well-written context and documentation like this is the key to unlocking consistent, high-quality AI results. Claude Code and Cursor now both have a planning mode that works a lot like Dreambase (ours is more fun though :)

Rethinking Engineering Team Structures for the AI Era: From my experience, a better strategy for organizing engineering teams is: Product Developers/Engineers: Frontend + Backend → Focused on building product features end-to-end. System Developers/Engineers: Backend + Infra → Responsible for designing and coding a robust, scalable platform. They handle integrations, optimize performance, and implement ci/cd, infrastructure-as-code, ensuring that the system architecture is solid, maintainable, and future-proof. QA Team: Full-spectrum testing across UI, backend, and databases → Ensuring quality at every layer. All teams report to one common lead / tech architect who oversees repositories and infrastructure, ensuring alignment across the stack. The future norm: AI tooling is mandatory across the stack. Product teams should integrate agentic workflows to their business products/apps and websites. System teams should ensure seamless support and scalability for these AI-driven workflows. I believe this structure balances speed, quality, and innovation—perfectly suited for modern AI-enabled product engineering.

Lately a few people have asked me what skill makes the biggest difference for designers working in design systems right now. My answer might surprise some: learn a bit of frontend. You don’t have to become an engineer or ship production code. But knowing how components actually work under the hood completely changes how you design. Here’s what I’ve noticed: • You can tell when AI tools give you messy or inefficient code. • You can talk with engineers (and AI) in the same language. • You understand how design tokens, props, and variables connect. • You can tweak small visual details yourself instead of waiting on a handoff. Once you understand the basics of code, you stop designing in isolation. You start designing systems that translate better, automate faster, and make more sense to everyone on the team. Design, engineering, and AI are blending together. Learning a little frontend is how you stay fluent in all three. The principles outlined in the photo are a great place to focus your attention as well. #DesignSystems #Design #DesignOps

Is your team still stuck in PRDs? We used to spend days writing lengthy requirement docs… and then wait weeks for design mockups. Now? AI + no-code tools let us skip the waiting game and go straight from requirement to prototype. Imagine this: you upload your PRD (or even just paste the key flows) and within minutes you’ve got a clickable, functioning Visual Requirement Doc (VRD is what i call it) ready to share with stakeholders. Tools like Lovable, Replit Agent, Bolt, and v0 are making this possible. They empower PMs, designers, and founders to spin up working interfaces and logic, straight from natural language. 💡 Why this matters: 🛡️ Less miscommunication → people can interact with the flow instead of debating text ⚡ Faster validation → spot UX gaps, test with users, and adjust before dev 🧩 Better collaboration → PMs, designers, and engineers align faster around a live prototype Of course, these prototypes aren’t production ready yet.... you’ll still refine and scale. But they’re redefining how we specify, validate, and align on product features. I tried it myself: turned a text PRD into a VRD (prototype) in under 10 minutes. Here’s the prompt I used with a PRD: Example: “AI Finder is a vertical search engine that helps users discover AI tools based on specific needs, delivering structured, contextual, and personalized results instead of endless browsing.” The result? A live prototype of the idea..... without writing a single line of code.

In-House Designers: How AI Can and Will Impact the Full Lifecycle of Your Work (Video Recc 📽️) This video helped me understand the way AI is helping close the gap between design and engineering. Two months ago, my team watched this together-- product, design and eng. all in one room-- and talked about it afterwards. Then I set up Claude Code, started editing the code base directly, and even pushed my own commits. Grab some folks from your engineering department and set a date for a watch party. Then start experimenting together! Thanks Meaghan Choi at Anthropic for sharing your process with us! Video linked in comments 👇

This is the most succinct and clear explanation as to why ai coding tools will make things much worse for companies that don't understand or manage Theory of Constraints. https://lnkd.in/gvxEA6TM