Most product organizations have some model of a evaluate course of. Usually, as soon as PMs have an early draft of a PRD (Product Requirement Doc) prepared, it’s circulated throughout design, engineering, authorized, operations, science, and product management. That course of is designed to enhance high quality and scale back danger. In observe, it typically reveals a tougher actuality: PMs is perhaps making selections in techniques the place the related context extends far past what anybody individual can simply assemble on their very own.
A PRD might attain the evaluate stage with an unsupported headroom assumption, a blind spot in how the characteristic might have an effect on adjoining techniques, an unexamined second-order impact, or a policy-sensitive change with out the guardrails reviewers anticipate. In different instances, the crew could also be unknowingly revisiting a speculation that was already explored in a smaller experiment or adjoining effort, however the related context is scattered throughout docs, decks, dashboards, and institutional reminiscence.
At that time, the evaluate course of tends to pivot to lower-level discovery work: surfacing adjoining impacts, reconstructing prior context, and figuring out questions that’d been extra helpful to handle earlier. That slows groups down, consumes reviewer consideration on points that might have been surfaced earlier, and makes suggestions inconsistent.
The actual drawback isn’t that PMs lack rigor. It’s that product work typically requires a 360-degree view that’s troublesome to assemble manually within the second: adjoining impacts, accomplice issues, prior experiments, hidden dependencies, and the questions senior reviewers are prone to ask.
That was the issue we got down to resolve.
Why This Issues at Uber
At Uber, product improvement runs via a structured checkpoint course of that provides management and cross-functional groups visibility, accelerates approvals, and drives constant execution. However a checkpoint course of is simply as efficient as the standard of the supplies coming into it.
We noticed a chance to strengthen that workflow additional by serving to PMs floor essential questions earlier. Moderately than altering the checkpoint course of itself, the aim was to enhance the standard of what entered it.
That led us to a easy query, and finally to the PRD Evaluator: what if each PM had a quick, contextual first-pass reviewer earlier than a PRD reached the broader approval course of?
Position of the AI-Powered PRD Evaluator
The PRD Evaluator is an AI-powered reviewer that begins with a PRD and assembles a broader information base round it: linked paperwork, associated decks and assembly notes, prior experiments, cross-functional artifacts, and preloaded Uber-specific context like core ideas, metric definitions, and key jobs to be completed. It makes use of that context to return a structured evaluation of launch readiness.
Its position is intentionally centered: strengthen the PRD earlier than it reaches high-cost evaluate boards. To not exchange senior judgment, however to assist groups enter these conversations with stronger context and fewer avoidable gaps. It sits upstream of the approval system and improves the standard of what enters it.
For us, that meant constructing a system that helps PMs do just a few issues earlier and higher:
- Establish a very powerful gaps in a draft
- Floor adjoining impacts and cross-functional dependencies
- Uncover prior learnings that is probably not apparent to the present crew
- Enter checkpoint and evaluate boards with a stronger artifact
How It Works: 4 Steps From Draft to Actionable Scorecard
We didn’t need a generic writing instrument that merely rewarded polished prose. A PRD might be well-written and nonetheless miss the context, framing, or resolution logic that determines whether or not it’ll maintain up in evaluate.
Determine 1: Overview of how the PRD Evaluator works.
1. Construct a Broader Data Base Across the PRD
The evaluator makes use of the PRD as an entry level, then harnesses AI to go looking throughout related firm artifacts and linked materials to assemble the context wanted to evaluate the choice nicely: associated paperwork, prior experiments, cross-functional inputs, and preloaded Uber-specific context.
2. Classify the PRD to Calibrate Evaluation Depth
Not each PRD wants the identical scrutiny. The evaluator classifies every proposal and calibrates accordingly:
- Lighter evaluate for UX parity or discoverability adjustments
- Reasonable evaluate for incremental workflow adjustments or inside tooling migrations
- Full evaluate for net-new capabilities
- Full evaluate with specialised scrutiny for coverage, pricing, or market adjustments
3. Assess Launch Readiness Throughout A number of Dimensions
The evaluate is structured round a number of dimensions together with:
- Alternative and Speculation: Is the issue actual, and is success outlined clearly sufficient to guage?
- Product Scope: Is the proposal comprehensible, well-scoped, and decision-ready?
- Consumer Expertise and Affect: Does the expertise work nicely throughout person segments, geos and potential edge instances?
- Metric and Information Rigor: Does the PRD outline success, guardrails, and a reputable validation strategy?
4. Produce a Scorecard Constructed for Motion
Moderately than a wall of feedback, the evaluator produces a structured scorecard:
- A launch-readiness score
- Dimension-by-dimension assessments
- A transparent “begin right here” pointer to a very powerful repair
- For every hole, share what’s lacking, present write-ready substitute textual content ideas, and proof from linked docs or prior experiments
- Prioritized motion objects cut up into vital necessities and optimizations
The output is designed to do greater than level out weaknesses. It’s meant to make the following spherical of revision simpler and extra focused, and the following evaluate dialog greater sign.
Determine 2: Abstract of the PRD Reviewer output format.
Determine 3: Illustrative scorecard instance.
The place the Worth Reveals up for PMs
The most important worth is that it adjustments the standard and timing of product considering.
It Expands a PM’s Subject of View
Most of the hardest product errors come from incomplete visibility. A PM might not know {that a} comparable speculation was examined earlier by one other crew. They might not understand a metric is ambiguous or lacking an apparent guardrail. They might not see a downstream operational dependency as a result of it sits exterior their speedy product floor.
A very helpful evaluator expands that subject of view. It may join a draft to prior artifacts, adjoining efforts, pre-existing hypotheses, and lacking questions, to which the creator has entry, that’d in any other case depend upon another person remembering them in a gathering. It may additionally floor context that was by no means explicitly linked within the PRD however continues to be related to understanding the choice.
It Makes Self-Evaluation Extra Structured
Most PMs can inform when a doc feels weak. The tougher query is why it’s weak and what to repair first.
The evaluator makes that prognosis extra specific. As an alternative of obscure unease, the PM will get a structured view of lacking fundamentals: unsupported headroom assumptions, undefined guardrails, blind spots in how a change might have an effect on adjoining techniques, or dangers that want acknowledgement.
It Improves the High quality of Evaluation Rooms
When a PRD reaches a reviewer in higher form, the dialogue strikes sooner towards tradeoffs, prioritization, and judgment, and fewer time is spent recovering context. That’s the place the evaluator connects most on to Uber’s product improvement system.
It Turns Critique Into Usable Revision
Crucial design alternative within the system wasn’t scoring. It was making certain actionability.
PMs don’t profit a lot from feedback like “be extra particular” or “suppose via draw back danger”. The evaluator is most helpful when it converts critique into revision steering: outline the baseline, identify the goal, add the guardrail, scope the primary launch extra narrowly, acknowledge the danger, or make the dependency specific.
That adjustments the workflow from passive critique to lively enchancment.
Early Adoption
Early utilization validated the core worth: the evaluator helped IC PMs uncover blind spots early, pressure-test unsupported headroom assumptions, floor how a proposed change might have an effect on adjoining techniques that weren’t core to their position, and determine expertise enhancements throughout the scope that they had already outlined.
In early inside utilization, the evaluator has already been utilized by dozens of PMs throughout Uber.
The instrument’s worth reveals up when PMs can convey it into their regular drafting and evaluate workflow, strengthen the constancy of what enters evaluate, and assist reviewers give attention to higher-signal questions.
What We Discovered
A number of classes stood out as we constructed and examined the evaluator:
- Frameworks beat generic critique. Broad feedback not often assist groups transfer sooner. The leverage comes from a framework tied to precise resolution standards and failure modes.
- Context issues as a lot as language high quality. Many essential indicators reside exterior the PRD itself, and richer context typically reveals a special set of blind spots than the doc alone.
- Onerous boundaries make output extra sincere. Defining a small set of vital gaps helped the evaluator keep away from calling a PRD review-ready when the basics have been lacking.
- Prioritization is a part of the product. A evaluate instrument that flags all the things as essential isn’t serving to. The evaluator’s worth comes from telling PMs what to repair first.
- The perfect AI output improves human conversations. The strongest signal the evaluator was working was that later evaluate discussions grew to become sharper and sooner.
The place Human Judgment Nonetheless Issues
The evaluator doesn’t goal to make ultimate guide approval selections or exchange area specialists. The instrument is most helpful when it strengthens the artifact earlier than skilled evaluate.
The toughest a part of product improvement is getting the fitting folks to make the fitting selections on the proper time, utilizing an artifact sturdy sufficient to help these selections.
Most product organizations have some equal of checkpoints, evaluate boards, or gated approvals. The names differ, however the problem is similar: how do you make sure that the artifact coming into the method is powerful sufficient for the method to do actual work?
AI has actual leverage right here as a structured thought accomplice that expands context, surfaces blind spots, and sharpens judgment earlier than a choice reaches a high-cost discussion board. That’s the reason we constructed the PRD Evaluator. And primarily based on what we’ve seen thus far, we predict this sample (AI that strengthens the enter to human decision-making) will matter nicely past one firm or one instrument.
Acknowledgments
Cowl Picture Attribution: Created by Gemini
Scorecard Pictures Attribution: Created by Claude