Manav Trivedi

When I started as a #PM, a large part of the role was writing #PRDs and iterating with stakeholders before handing off to engineering. That process is rapidly changing and PRD's as we know it are dying. Here’s what will replace it. 📋 FROM: Traditional PRD process 📋 ■ Spending hours interviewing customers and manually synthesizing insights ■ Drafts that miss standard language and cross-stakeholder expectations ■ Low-fidelity or no visuals in early drafts ■ Lengthy, repetitive review cycles with multiple stakeholders ■ Manual updates after every meeting ■ Manual breakdown of PRDs into features and user stories ■ Manual creation of QA test cases and UAT scripts ■ Static, outdated documentation post-launch ⚡ TO: GenAI-enabled PRD process ⚡ ■ AI-augmented synthesis with interrogation and summarization capabilities ■ GPT-powered co-creation that aligns across stakeholders and domains ■ Instant generation of interactive prototypes for each feature or improvement ■ Real-time prompt refinement during working sessions to shape ideas ■ Automated artifact updates from meeting transcripts and decisions ■ Auto-generation of features, user stories, and acceptance criteria ■ Automated creation of QA test cases and UAT scripts ■ Continuously evolving, real-time documentation synced with changes 🧠 What it means for PMs 🧠 1️⃣ You’re no longer measured by specs, but by how fast you validate and ship ideas 2️⃣ Documentation isn’t dead, it’s now a dynamic, AI-supported asset 3️⃣ The best PMs don’t “own the doc”, they orchestrate the system Shai Dinnar Diana Cordova Shivani Rathi Emily Gao Dane Greisiger Justine Wang Kushal T. Anne Schmitt Bradley Antcliff Dimitrios Lippe

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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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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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for the past year, I have seen too many startups falling into the trap of "shipping the AI PRD." let me explain: a PRD (product requirement document) is what a PM writes to communicate their vision to engineering and design teams. there are startups building AI that helps PMs chat and generate a PRD faster. here's the problem: that's a transient bottleneck. because tools like Lovable are skipping the PRD entirely. you don't need a document when you can show and communicate way better. when you're building with AI, you have to avoid building the "AI version of the old process." you have to ask: how do i circumvent this entire process and rip it apart at its seams? that's the long-lasting strong company. examples of AI PRDs to avoid: - AI that makes meetings more efficient (vs removing meetings) - AI that speeds up handoffs (vs removing handoffs) - AI that writes better docs (vs removing docs) the question isn't "how do we make the old way faster?" the question is "what becomes possible when we remove the constraint entirely?" that's how you find the real opportunity.

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Enterprises like Apple built their brands on deterministic, pixel‑perfect products. GenAI breaks that model. Experience scope no longer fits inside a classic PRD; it's probabilistic. One tap can now trigger a trillion‑parameter model and a range of outcomes. Users' expectations have shifted - they want a conversation, not a canned path. The old playbook - static specs, pass/fail QA, A/B tests - can't keep up. What replaces it? - Build while you write - PMs and engineers should spin up prototypes as quickly as they draft specs. In the enterprise, permissions, API wiring, and infrastructure is gating vibe-coding, so invest in a "vibe‑coding" devX that removes friction. - Dynamic UX design - craft guardrails, not linear flows. - Continuous eval loops - synthetic data, red‑team stress tests, human‑in‑the‑loop feedback, real‑time metrics. - Dev Ops + AI Ops - ship model + data + prompts + eval harness, not just code. - Quality: shift from testing responses to testing experiences - It's not about rigid input-output anymore. It's about guardrails and transparency, testing that dialog-level trust and coherence, not static responses.  - Generalists over Specialists - we will see roles boundaries continue to blur. Those that cling to deterministic comfort and old model (PRD → design specs → release) will drift behind and watch relevance erode - while they add one feature, more GenAI native competitors add entire experiences.

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