Kos Vibhute

As product manager roles are transmutating from a pure manager type role to a PM engineer type role, here's the checklist for you - 1. Everyday play and experiment with different AI tools, internally make a list on which model to use for what purpose (this step is critical, as this compounds). 2. Never use AI for the initial draft when you are writing something. Writing brings you detailed clarity and in this AI age, clarity is king. Finish up your draft with AI, but start manually. 3. Always pay and use the frontier model. Don't cut corners here. The output of an Opus 4.6 is drastically different from sonnet when it comes to complex tasks. Having said that, develop a mental map on which model to use for what complexity of tasks. Otherwise you will just burn tokens for simple tasks. 4. When learning or understanding a concept, be deliberate and progressive when using the AI tools as well. For example, start with ChatGPT, take that output, understand it, add your own thoughts and then go to claude. Again point number 3, do apples to apples. If you are using the free tiers, treat all the free tiers as equals. 5. Learn to write agent native specs. Agent native specs are living docs which live in the repo, which the agent has direct access to. Take care of the smaller nuances. For example - If your team is using a context window of 100k models, then don't write specs which are too large. Eventually the agent will run out of context and start hallucinating. 6. When you have developed a strong intuition about points 1-5, then and only then build. The world is moving away from a PRD driven build environment to an agent spec driven build environment. Internalise that change. Change your workflow to adapt to it. 7. Build prototypes with the speed of thought, but remember these are prototypes and not actual production code. The prototype to production code journey is a bit far along. But that doesn't mean, you don't build, build your ideas quickly. Again the idea here is for you to develop enough intuition and judgement so that you can keep up with the agent. 8. As we are shifting from "vibe coding" to "agentic engineering", please remember this shift is going to be chaotic. Remember the world required 1-2 yrs to fully understand what actually is an "agent". So don't get overwhelmed, don't get any FOMO. Remember there's still a lot of time for the world to make this transition. All you can do is prepare better. Finally, anyone who is transitioning from a traditional PM to an AI native PM builder, remember, "chaos is a ladder". Be curious, be eager to learn, be adaptable, be flexible. Spend a lot of your time with the AI tools, stress test them, understand their limits, understand their context window and memory. This is in no way any exhaustive list (in fact I would argue this is a beginners list). But as you climb each stair in this ladder, you will develop enough intuition to move even further.

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Bangalore PMMs, Product Marketing over Chai is back: Workshop-style. On Sept 14 in Bangalore, PMM over Chai is hosting a hands-on format meet-up designed to help new and seasoned PMMs: — Prep for interviews and assignments — Practice real frameworks in a safe space — Build a more strategic lens on their work Our first Workshop Edition. Because let’s be honest: reading about frameworks is easy. Actually practicing them, getting feedback, and seeing how others apply them is what really matters. Instead of panels or talks, we’ll break into small groups to tackle real PMM challenges, from building a GTM plan to revamping product messaging. And then peer-review each other’s work. Think of it as a PMM workout. Less theory, more doing. September 14 | HSR Layout, Bangalore Register here for details: https://luma.com/7d7hcx33 Note: Since we’re moving into a co-working space for these workshops, there’ll be a small entry fee to pay the venue. Have a framework you want to try? DM me or comment below to get added to our WhatsApp group, and let’s turn your framework into a breakout workshop. Sachin Jha Tanwistha Gope #ProductMarketing #PMMCommunity #ProductMarketingOverChai #PMMLife #BangaloreMeetup #GTM #Positioning

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Product management is rapidly evolving in the age of AI. Not long ago, writing a detailed PRD could take hours, or even days. Today, with tools like ChatPRD, those requirements can be drafted in minutes. This change unlocks enormous speed and frees PMs to focus more on strategic alignment, customer insights, and decision-making. But there's a tradeoff: while AI accelerates content generation, it can also shift where the real work happens. For example, tagging logs and setting up robust evaluation frameworks, which are critical for ensuring AI outputs are accurate and effective, can still take hours of careful effort. In this new reality of product management with AI, some tasks are collapsing in time, while others are becoming even more important. The PM toolkit is quickly expanding from spec writing toward orchestrating evaluation workflows, data quality strategies, and feedback loops that ensure AI-driven products truly deliver. The PM role is evolving requiring renewed focus on systems, evaluation, and outcomes. Are there other shifts that you have noticed?

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The product opportunity is shifting from building on models to building with agents. 1. The Product Shift: From Tools to Agents The deep dive with Anthropic Generative BLR: Fireside Chat Lightspeed  was pivotal. The adoption curve of tools like Claude Code and the advances in Sonnet 4.5 signal the end of the "simple prompt-and-response" era. For PMs, this means: We must pivot from designing static features to designing autonomous, reasoning agents. Our product specs now involve defining multi-step goals, guardrails, and success metrics for AI that executesworkflows, not just assists with single tasks. The Business Value: The core value proposition is no longer speed, but end-to-end productivity and reliability. 2. The Market Opportunity: AI as Core Infrastructure /function1 underscored the universal integration of AI. Discussions on Decentralized AI, Robotics, Security, and Human-AI Collaboration show AI moving beyond niche apps and into core operating systems for industries. For PMs, this means: We must identify deep integration points within vertical SaaS and enterprise systems where AI can become an invisible function layer, solving fundamental business problems (e.g., automated supply chain optimization, autonomous code remediation). The Strategy: The winning products will embed AI so effectively that the user stops calling it "AI" and starts calling it "the way we do business." My key takeaway for the Product community: The next 12-18 months will define the winners. We need to stop building wrappers and start building intelligent, reliable, and measurable solutions that leverage autonomous AI to deliver step-change user value. Behavioral Signals AI/ML API Future AGI BrainSpaceNVIDIAAnthropicAgents Virtual MachinePalantir Technologies Hemant Mohapatra Daniel Delaney Jugal Shah Ahmed M.Rana Gujral Nikhil Nainani Yuval D. Nikola PetrovicVrinda Damani #ProductManagement #ProductStrategy #GenerativeAI #AIAgents #Anthropic #function1 #Innovation #TechTrends

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We love Lovable ❤️ From MVP ➜ MLP — this is the real shift in product building. ✨ Anyone can launch a Minimum Viable Product. But today, viable is not enough. Users want Minimum Lovable Products — products that feel good, look good, and create an emotional connection from day one. When you focus on lovable, everything changes: 🚀 Faster adoption 💡 Better feedback 🤝 Strong loyalty 🔥 Higher retention ✨ And users actually enjoy the experience The future belongs to products people love, not just tolerate. Ready to build your first AI MLP product ? https://lnkd.in/drsJq5Wj #ProductManagement #MLP #Innovation #DesignThinking #AI #SaeedAlHasanAiMaster

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Recently had a chance to sit with Joy Banerjee, VP Product Design at Zomato and it was one of the most informative power packed conversations I’ve ever had about product, design, human behaviour and everything in between! What started as a 60 mins conversation on a Sunday, extended far beyond 5 hours talking about how the product/design landscape is changing today, learning from his 2 decade plus of experience building products and shaping behaviours for 100s of millions of users. He had a refreshing take on how design as a field has been such native to India [think Indian architecture] and hence it shouldn't be looked with a myopic view of only optimising funnels. Its a much larger responsibility and learning from nature should help us design behaviours that sustainable and human-centric. Some of the key insights we discuss on how to build products : ✅ The 3C Framework — Choice, Control & Clarity Indian consumers appreciate product flows that give them enough information to feel confident in making high-value purchases. At Cleartrip, reducing friction actually reduced bookings because users wanted to double-check details. Joy shared - give users choice, control, and clarity to help them make decisions. That’s what creates delight for high-value actions. ✅ Honesty is Still the Best Policy! Cancelled orders were leading to massive food wastage at Zomato. Joy’s team introduced the now-famous “Food Rescue” feature - openly telling users they were rescuing cancelled meals. Earlier there was massive apprehension how users would take it however the result: huge reduction in food waste and users loved the chance to be part of something noble and meaningful. ✅ Design Shapes Human Behaviour — So Shape It Right Features like the ‘Like’ button or ‘Order Now’ have permanently shaped how people behave online. Joy reminded us that every design decision has the power to shape behaviour forever - and we must be intentional about the why behind every choice. ✅ Live Your Users Understanding how Indian users engage means living their behaviours - not just surveying them. Joy highlighted shifts like voice-first usage (voice notes for delivery partners) and more visual, prominent cues as patterns that emerged only by closely observing users. Great design comes from living your users and then designing for them! Joy also touched about Shape coding and how this interesting field led to so many physical product becoming successful - by mimicking how humans were already behaving in the world. Thanks to Rati Murti and Team for organising this. Link to both the episodes in comments. #productdesign #product #design #psychology

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PRD templates for product managers. Bookmark these A Product Requirements Document (PRD) is one of the most essential documents in product work. It sets out the plans for a team that is ready to move from ideation to implementation of a feature or product. There are many different approaches to creating a PRD. They mainly differ in the level of detail and length of the document. There is no universal solution, but you can choose a template from our collection that intuitively seems most suitable depending on the complexity of the project, the number of participants and the specifics of your company’s processes. https://lnkd.in/e9PeqsPF

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Just completed the Product Discovery Certification Cours course by Pendo.io (hashtag#MindtheProduct), Here are my key learning: * Product discovery is the crucial process of deeply understanding customer problems and validating solutions before development. It strategically mitigates key risks, ensuring features drive real customer value and positive business impact * Product managers face a unique challenge: They must design features that drive customer value and advance key business goals * Beware of Cognitive Biases: These systematic errors in thinking can misalign what you build with customer needs or business goals * Examples include: * Confirmation bias: Seeking feedback that confirms your existing views * Sunk cost fallacy: Continuing a project due to prior investment, even if it's failing * Authority bias: Giving unmerited weight to opinions of those in authority * Product Discovery is Crucial: It's the process of understanding customer problems and needs, then validating or rejecting solutions before development. This helps eliminate four big risks: value, usability, feasibility, and business viability * Effective Discovery Drives Business Impact: It leads to positive outcomes like customer retention, revenue growth, reduced costs, and risk mitigation * Embrace a Data-Driven Approach: Product discovery relies on four key types of data: Research, Customer Feedback, Product Analytics, and Testing * AI Optimizes Discovery: AI tools can accelerate the process, include more inputs, and make discovery more continuous * The 6-Part Product Discovery Framework guides the process: * Define the outcome * Understand users’ problems * Identify the problem to solve * Explore possible solutions * Test and validate solutions * Close the loop * The four collaborative tools and visualizations used in product discovery to better understand users, ideate solutions, and manage risks are: * Empathy Map * User Journey Map * Opportunity Solution Tree * Assumption Mapping #productdiscovery #AIinProductDiscovery #continuouslearning

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