Such a valuable resource! AI is transforming product development, and having a structured PRD template makes the process so much more efficient. Thanks for sharing!
Hello Pawel, thank you for collaborating with a Product Leader from OpenAI and sharing this with everyone!
I have a general question related to PRDs that's been bugging me.
I am an Associate Product Manager at the moment and have worked with 4 tech companies so far. Unfortunately, a PRD has never been needed in my life.
Usually all requirements come from the senior stakeholders and they already have a good understanding of the market, the strategic context, and the WHY of what we're building.
What is usually required are a set of functional and non-functional requirements to be passed on to the Devs.
So when exactly do we create a PRD? When does it become necessary to create such a detailed document?
Hey Daniyal, can you see my other comment regarding the scope, the size, and when it makes sense to create a PRD?
When it comes to requirements coming from the stakeholders, it's a separate issue. In most cases, you should get objectives to achieve with the desired outcomes and discover how to achieve them: https://www.productcompass.pm/p/what-exactly-is-product-discovery
While in practice some features are no-brainers and stakeholders can provide great insights, implementing their requests, especially without validation, shouldn't be the default approach. See: https://www.svpg.com/empowered-product-teams/
Hey Thomas, the risks are included in the product scope. They are tackled as part of continuous discovery.
If needed, you can adjust this template to your specific needs and context.
The AI PRD might seem large, but if you strip it down to just "example responses" and headers, it's actually quite minimal. Most sections are only 2–3 paragraphs! 😊
As visualized in the post, the PRD evolves as you learn more. The goal is to build alignment, not to document every possible detail (e.g., User Stories).
And frankly, for standard features that can be tackled in 1-2 Sprints, I have never considered writing a PRD. In many cases, sharing a design and a loom video or/and a Slack message was enough.
But when building a broad alignment becomes both challenging and critical, like with Auto Write, documenting, communicating, and aligning around the key assumptions and learnings becomes essential.
Much appreciate the template and real-world example.
My main takeaway is that, other than the specific model and system performance requirement (relevant for viability and feasibility check) called out in section 8, "No Functional Requirement", the AI-empowered Product PRD resembles any good PRDs in general.
In other words, the core skills and, particularly decision -making framework, to excel in PMing stays the same and perhaps even more important in this new era when low level tasks and decision making, could be offloaded to AI agents.
It was great collaborating with you on this Paweł, thanks for having me on the Product Compass newsletter!
Thanks, Miqdad. It was amazing to work together. The first collab is out! 💪😊
Such a valuable resource! AI is transforming product development, and having a structured PRD template makes the process so much more efficient. Thanks for sharing!
Hello Pawel, thank you for collaborating with a Product Leader from OpenAI and sharing this with everyone!
I have a general question related to PRDs that's been bugging me.
I am an Associate Product Manager at the moment and have worked with 4 tech companies so far. Unfortunately, a PRD has never been needed in my life.
Usually all requirements come from the senior stakeholders and they already have a good understanding of the market, the strategic context, and the WHY of what we're building.
What is usually required are a set of functional and non-functional requirements to be passed on to the Devs.
So when exactly do we create a PRD? When does it become necessary to create such a detailed document?
Hey Daniyal, can you see my other comment regarding the scope, the size, and when it makes sense to create a PRD?
When it comes to requirements coming from the stakeholders, it's a separate issue. In most cases, you should get objectives to achieve with the desired outcomes and discover how to achieve them: https://www.productcompass.pm/p/what-exactly-is-product-discovery
While in practice some features are no-brainers and stakeholders can provide great insights, implementing their requests, especially without validation, shouldn't be the default approach. See: https://www.svpg.com/empowered-product-teams/
Thanks for sharing with our PM community!
Real-life based on Shopify also very valuable.
A few comments:
- Where do you capture « risks » (internal/external) in this approach?
- Based on experience, producing a full AI PRD early in product Life is time consuming:
- What is the right time / prerequisites to launch this work ?
- What would be a lighter version of it, if relevant?
Hey Thomas, the risks are included in the product scope. They are tackled as part of continuous discovery.
If needed, you can adjust this template to your specific needs and context.
The AI PRD might seem large, but if you strip it down to just "example responses" and headers, it's actually quite minimal. Most sections are only 2–3 paragraphs! 😊
As visualized in the post, the PRD evolves as you learn more. The goal is to build alignment, not to document every possible detail (e.g., User Stories).
And frankly, for standard features that can be tackled in 1-2 Sprints, I have never considered writing a PRD. In many cases, sharing a design and a loom video or/and a Slack message was enough.
But when building a broad alignment becomes both challenging and critical, like with Auto Write, documenting, communicating, and aligning around the key assumptions and learnings becomes essential.
Much appreciate the template and real-world example.
My main takeaway is that, other than the specific model and system performance requirement (relevant for viability and feasibility check) called out in section 8, "No Functional Requirement", the AI-empowered Product PRD resembles any good PRDs in general.
In other words, the core skills and, particularly decision -making framework, to excel in PMing stays the same and perhaps even more important in this new era when low level tasks and decision making, could be offloaded to AI agents.