How to Create an AI-Friendly PRD
Learn the core principles behind AI-friendly PRDs—why they matter, how they shift your workflow, and how to use AI as a reliable junior developer.
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When you add AI into your dev workflow, the bottleneck stops being “how fast can we type code” and becomes “how clearly can we define the work.”
That’s where an AI-friendly PRD comes in.
We’re not reinventing the PRD. We’re tightening it around what AI needs to do its job well, while humans stay focused on the high-leverage work: client conversations, framing outcomes, and guarding quality.
What We Mean by an “AI-Friendly PRD”
At Inovo, an AI-friendly PRD is a single source of truth that a language model can parse without guessing.
A few key traits:
- It reads like a compact spec, not a novel.
- It includes only the Shape Up Framing blocks:
- The work is broken into clearly named sections, each with 3–5 atomic tasks.
- Each section carries a one-word status: Not Started, In Progress, or Done.
It’s small, readable, and structured enough that:
- AI can load it as context.
- Humans can scan it quickly.
- Handoffs between sessions or teammates stay clean.
Why We Use AI PRDs at Inovo
We don’t use AI to “figure out the project.” We use AI to execute the parts we’ve already figured out.
An AI-friendly PRD lets us:
- Offload repeatable, well-defined coding to the model
- Stop re-explaining the same feature in every new Cursor session
- Lean into a Shape Up mindset: fixed time, flexible scope
- Keep client, PM, dev, and AI working off the same picture
The downhill work—implementing what’s already clear—is where AI shines.
How the Human → AI Split Actually Works
The dividing line is simple:
Humans handle fuzziness. AI handles clarity.
Humans own:
- Client communication and expectation setting
- Root-cause digging (“Why is this actually a problem?”)
- Outcome framing and constraints
- Final code reviews and architecture decisions
- Code generation once tasks are concrete
- Iteration within the boundaries we’ve set
- The grunt work that would normally chew up hours
An expert developer always reviews the diffs. No “vibe coding,” no “it compiled, ship it.”
Think of it like running a team of junior devs: you delegate clearly, review rigorously, and keep them pointed at the right things.
How the Developer Role Evolves
Once you start working this way, the definition of “senior dev” changes.
If you’re leveraging AI effectively, you’re essentially promoted to Senior whether your title changes or not. Your core skills become:
- Defining crisp, unambiguous tasks
- Spotting missing edge cases and constraints
- Reading and reviewing diffs quickly
- Unblocking the AI when it gets stuck or confused
- How clearly you framed the work
- How smoothly the AI could execute
- How much throughput you unlocked for the team
Clarity is.
Using Shape-Up GPT to Draft the PRD
We don’t write every PRD from a blank page. We use a Shape-Up-style GPT to get a structured first pass.
The flow looks like this:
- Fill out a small input block: title, one-line pitch, appetite, and (optionally) a task grid.
- The GPT returns a framing-only PRD plus sections and tasks—no extra fluff.
- We paste that into Cursor and interrogate the bot:
- We edit the tasks in plain text until the PRD is crystal clear.
If you want the detailed walkthrough—how we prompt it, how we structure sections, how we use it with real projects—the training video goes deep on that.
Cursor Workflow in Practice
Once the PRD is solid, we move into Cursor. A few practices have emerged as non-negotiable:
- Review the PRD slowly. Better questions now save a lot of chaos later.
- Start a new Cursor session per section and paste the PRD so context travels with you.
- Run the work a section at a time and commit only after tests pass.
- Keep the entire feature on a dedicated git branch so you can reset if AI goes off the rails.
“This is the slice of the world we’re touching right now.”
The Core Idea
At the end of the day, an AI-friendly PRD comes down to three things:
- A compact spec that captures the problem, outcomes, appetite, and what we’re not doing
- A sectioned roadmap with small, atomic tasks AI can execute cleanly
- Simple status hooks so humans and machines can track progress together
AI supplies scalable execution.
Treat the model like junior talent: delegate clearly, review ruthlessly, and document what changed. The more disciplined you are about the PRD, the more leverage you’ll get from AI.
For the step-by-step “how we do it” and a live demo inside Cursor, you can watch the full training.
Interested in joining our team? Apply below 🔽