When deep learning first showed up, it felt like magic. Suddenly, machines could recognize objects in photos, something traditional algorithms just couldn’t do. The lesson was simple: instead of coding rigid rules, let the machine learn from examples.
Now, we’re at a similar point with Large Language Models (LLMs). They can take messy, unstructured input: notes, meeting transcripts, emails and turn it into clear, structured output.
For product managers, that means transforming raw thoughts into Product Requirement Documents (PRDs) in minutes instead of hours.
The Old Way vs. The New Way
Old way: PMs spent 10–15 hours a week writing and editing PRDs. Collecting notes, structuring them, formatting. It was slow and draining.
New way: PMs can brain-dump their ideas in plain language. AI turns it into a structured draft, goals, requirements, success metrics, risks. And if needed, it can pull context from support tickets, surveys, or past docs.
This shift isn’t just about saving time. It’s about moving from “writing to think” toward “thinking and letting AI do the writing.”
A Quick Example
PM’s input (natural language):
“We should let users save articles for later. They often leave mid-read and come back later, but right now they lose their place. Maybe start simple with a bookmark button.”
AI’s draft PRD:
- Goal: Improve retention by letting users save articles.
- Requirement: Add a bookmark button. Saved articles appear in user profile.
- User Story: As a reader, I want to save an article so I can finish it later.
- Success Metric: 20% of active users use bookmarks in the first month.
- Risks: Could clutter UI; need balance between visibility and simplicity.
The PM then reviews, tweaks, and adds nuance (design trade-offs, rollout strategy).
Why AI Supercharges PRDs
- No blank page problem → Instant first draft.
- Data-driven → AI can pull insights from feedback, surveys, reviews.
- Clear and consistent → Standard format, consistent tone.
- Less busywork → Formatting and summarizing are automated.
- More strategy time → PMs focus on prioritization, trade-offs, and vision.
How to Use AI Wisely
- Start messy, not structured. Let PMs brain-dump. AI will organize.
- Iterate. The draft is a start, not the finish. Refine, sharpen, question.
- Add the human layer. AI doesn’t know your team, constraints, or market subtleties. That’s your job.
The Bottom Line
AI for PRDs isn’t about replacing PMs. It’s about removing friction.
The PM still owns the thinking. AI just translates it into structure, clarity, and consistency and act as a assitant to gather more context.
Used right, this means faster PRDs, sharper requirements, and more time spent on actual product strategy.
Sources & other readings
OpenAI. (2023). GPT-4 technical report*. OpenAI.*
Product School. (2024). How product managers write effective PRDs*. Product School.*
Atlassian. (2023). Using AI to improve product documentation and requirements*. Atlassian Corporation.*
Stanford Institute for Human-Centered AI. (2022). Human–AI collaboration and decision support systems*. Stanford University.*
