AI‑Driven Docs Replace PRDs
Key Points
- AI will let teams skip traditional product‑engineering artifacts like PRDs and one‑pagers because LLMs can efficiently translate meaning between stakeholders.
- The speaker proposes that high‑quality customer‑facing documentation become the central artifact, serving as the source for UI designs, technical requirements, and product rationale.
- By writing precise prompts, PMs can let LLMs generate and adapt documentation into the various required formats, shifting the PM’s role to providing intent and taste rather than producing multiple hand‑crafted documents.
- Though controversial and not immediate, this vision redefines the “definition of done” as a product created through AI‑interpreted docs rather than a collection of completed artifacts.
Sections
- Rethinking Product Artifacts with AI - The speaker proposes that AI’s efficient information exchange could render traditional human‑focused documents (PRDs, one‑pagers, test cases, etc.) obsolete, urging a radical overhaul of product‑engineering collaboration.
- Shifting Focus from Ideation to Execution - The speaker argues that the conversation must move beyond AI‑centric top‑of‑funnel experimentation toward clear documentation, well‑mapped product requirements, and systematic workflows that efficiently turn ideas into launched products.
Full Transcript
# AI‑Driven Docs Replace PRDs **Source:** [https://www.youtube.com/watch?v=Y_k6bDZZuQs](https://www.youtube.com/watch?v=Y_k6bDZZuQs) **Duration:** 00:07:14 ## Summary - AI will let teams skip traditional product‑engineering artifacts like PRDs and one‑pagers because LLMs can efficiently translate meaning between stakeholders. - The speaker proposes that high‑quality customer‑facing documentation become the central artifact, serving as the source for UI designs, technical requirements, and product rationale. - By writing precise prompts, PMs can let LLMs generate and adapt documentation into the various required formats, shifting the PM’s role to providing intent and taste rather than producing multiple hand‑crafted documents. - Though controversial and not immediate, this vision redefines the “definition of done” as a product created through AI‑interpreted docs rather than a collection of completed artifacts. ## Sections - [00:00:00](https://www.youtube.com/watch?v=Y_k6bDZZuQs&t=0s) **Rethinking Product Artifacts with AI** - The speaker proposes that AI’s efficient information exchange could render traditional human‑focused documents (PRDs, one‑pagers, test cases, etc.) obsolete, urging a radical overhaul of product‑engineering collaboration. - [00:05:26](https://www.youtube.com/watch?v=Y_k6bDZZuQs&t=326s) **Shifting Focus from Ideation to Execution** - The speaker argues that the conversation must move beyond AI‑centric top‑of‑funnel experimentation toward clear documentation, well‑mapped product requirements, and systematic workflows that efficiently turn ideas into launched products. ## Full Transcript
It's spicy take time. I want to share a
spicy take on where product is going.
And no, it has nothing to do with
AIdriven product discovery getting
shortened by tools like lovable. I've
heard that take. I think it's true. I
just think it's been overtalked about. I
want to share something a little bit
more controversial.
I think that we may need to change some
of our core artifacts when it comes to
product and engineering collaboration
because AI is making it so efficient to
translate and deliver information back
and forth. And so I think that a lot of
our old artifacts assume it's really
hard to convey meaning. So the PRD
product requirements document, it's
written specifically so that an engineer
can read it in a certain way. The one
pager is written specifically so the
business can read it in a certain way.
The test cases that we write for QA are
written specifically so that quality
assurance can read it in a certain way.
The press release we may write if you're
from a certain tradition like Amazon,
same deal. You get the idea. artifacts
written with the assumption that they
are human readers. You have to write for
a human audience. This is deliberately
provocative. I'm not saying we all live
into this world today. But I want you to
think about the idea of jump jumping all
of that, leapfrogging all of it and
getting to the end of the process. And I
asked myself that question. What does it
look like to leaprog away from this idea
of
the definition of done for a PM being
like an artifact produced that then gets
turned into a product and then the
definition of done at the end is like
launching the product. Well, how do you
how do you jump to the end of that with
AI in a way that's efficient?
My proposal for you is that the way to
do that is actually to have the
documentation for the customer become a
core artifact in the product development
process. So think about it. If you write
excellent docs like Stripe
does and you have an extraordinary
degree of fidelity in those docs, maybe
it's a technical product and you have
highderee fidelity and technical docs,
maybe it's a less technical product, but
the docs are still very very good for
your
audience. What more do you
need? Imagine it. You write an excellent
prompt. You understand what you need to
build so well that you can
actually write extraordinary
docs as a
PM. You can derive a user interface off
of
docs. You can derive technical
requirements off of docs if they're
written well enough.
you can derive the reason why you're
building this off of
docs because they are so clear about the
user
experience. And maybe that's what I'm
getting at. The heart of this is that
the prompt allows us to move to a world
where the LLM is doing the translation
back and forth between different
contexts. And we are the ones who are
simply providing taste and intent. I
know that's a provocative idea. I'm not
saying that tomorrow we're going to get
to a world where PMs are going to just
write a prompt, understand what they
want to build so clearly that they can
get to excellent docs that are prompt
driven, polish those docs till they
shine, and then engineers will be able
to just derive technical requirements
off of it, and we'll be able to derive a
business case. I'm
not. It's always going to be more
complicated than that. But as a thinking
exercise, I think it's really helpful
because I think it pushes us to stop
assuming that AI disruption is only
happening at the top of the development
funnel, which is where a lot of PMs have
stopped right now. Uh, and I think it it
pushes us to think about the end product
more. the way we can shortcut to
customer value with AI. And I think if
you really want to be disruptive and get
ahead of where AI is going, that is the
kind of thinking that you have to
practice. You have to start asking
yourself where is the end customer value
here. If we've been making all of these
documents under this outdated assumption
that humans need to read it, maybe we
need to stop doing that because maybe
human understanding only matters at
certain points in the workflow and maybe
we can derive that more efficiently in
other
ways. And
so for me, I think that I am going about
halfway into that world right now. I
think it's an experimentation thing. I
am starting to play with the idea that
any product document needs to
include
docs. If you're going to present a PRD
to an engineer, they should be able to
imagine and
understand the world that you want to
create through the help docs and the
onboarding docks and the guidelines that
are going to get the customer into it.
It should be that
clear. they should be able to click in
and say, "Okay, I can onboard this way."
Uh, and these are the edge cases that
we're covering in the help docs, and
this is the FAQ section in the help
docs,
etc. And by the way, I think if you
write that well, it maps really cleanly
onto good product
requirements. And so, I actually don't
know that it's a ton more
work. I'll leave it there. I I think
that I would like to get a conversation
going that goes past what feels like the
tired edge of the conversation right now
because the conversation right now
honestly with product management and
engineers and design and AI is obsessed
with the top of the funnel. It's
obsessed with wow can we get more
disruption and more optionality and more
rapid prototyping at the top of the
funnel. And I have talked with PMs who
are like, well, this this just makes the
top of the funnel really fat and we have
no way to actually refine and execute
and build efficiently out of that. And I
know that tools like cursor help a bit,
but we don't really have a systematized
way as a community of driving that
workflow forward to production. And I
think I want to push for that kind of a
conversation. And so this is a thinking
exercise. Push back, challenge, suggest
better ways to do it. But at the end of
the day, we need to be thinking about
the final results and how we can more
efficiently drive product execution to
launch and not just product ideation,
which is I feel like where most of our
AI thinking is going right now. And I
think it's it's not correct. I think
it's not the the only way to think about
what AI can do. Cheers.