Most product organizations have some model of a evaluate course of. Sometimes, 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 threat. In observe, it typically reveals a more durable actuality: PMs is perhaps making choices in programs the place the related context extends far past what anyone individual can simply assemble on their very own.
A PRD may attain the evaluate stage with an unsupported headroom assumption, a blind spot in how the function may have an effect on adjoining programs, an unexamined second-order impact, or a policy-sensitive change with out the guardrails reviewers count on. In different circumstances, the staff 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 deal with earlier. That slows groups down, consumes reviewer consideration on points that might have been surfaced earlier, and makes suggestions inconsistent.
The true downside isn’t that PMs lack rigor. It’s that product work typically requires a 360-degree view that’s tough to assemble manually within the second: adjoining impacts, associate issues, prior experiments, hidden dependencies, and the questions senior reviewers are more likely to ask.
That was the issue we got down to remedy.
Why This Issues at Uber
At Uber, product improvement runs by way of 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 barely as efficient as the standard of the supplies getting into it.
We noticed a chance to strengthen that workflow additional by serving to PMs floor essential questions earlier. Quite than altering the checkpoint course of itself, the purpose was to enhance the standard of what entered it.
That led us to a easy query, and in the end 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 data 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 executed. It makes use of that context to return a structured evaluation of launch readiness.
Its function is intentionally targeted: strengthen the PRD earlier than it reaches high-cost evaluate boards. To not change 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:
- Determine 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 staff
- 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 will be well-written and nonetheless miss the context, framing, or determination 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 look 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 Assessment Depth
Not each PRD wants the identical scrutiny. The evaluator classifies every proposal and calibrates accordingly:
- Lighter evaluate for UX parity or discoverability adjustments
- Average 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 judge?
- Product Scope: Is the proposal comprehensible, well-scoped, and decision-ready?
- Person Expertise and Affect: Does the expertise work nicely throughout person segments, geos and potential edge circumstances?
- Metric and Knowledge Rigor: Does the PRD outline success, guardrails, and a reputable validation strategy?
4. Produce a Scorecard Constructed for Motion
Quite than a wall of feedback, the evaluator produces a structured scorecard:
- A launch-readiness ranking
- 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 alternative textual content solutions, and proof from linked docs or prior experiments
- Prioritized motion gadgets cut up into important necessities and optimizations
The output is designed to do greater than level out weaknesses. It’s meant to make the subsequent spherical of revision simpler and extra focused, and the subsequent evaluate dialog increased 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 pondering.
It Expands a PM’s Subject of View
Lots of the hardest product errors come from incomplete visibility. A PM might not know {that a} related speculation was examined earlier by one other staff. They could not notice a metric is ambiguous or lacking an apparent guardrail. They could not see a downstream operational dependency as a result of it sits outdoors their fast product floor.
A very helpful evaluator expands that area of view. It may possibly 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 possibly 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-Assessment Extra Structured
Most PMs can inform when a doc feels weak. The more durable query is why it’s weak and what to repair first.
The evaluator makes that prognosis extra express. As an alternative of imprecise unease, the PM will get a structured view of lacking fundamentals: unsupported headroom assumptions, undefined guardrails, blind spots in how a change may have an effect on adjoining programs, or dangers that want acknowledgement.
It Improves the High quality of Assessment 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
An important design selection 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 by way of draw back threat”. The evaluator is most helpful when it converts critique into revision steering: outline the baseline, title the goal, add the guardrail, scope the primary launch extra narrowly, acknowledge the chance, or make the dependency express.
That adjustments the workflow from passive critique to energetic 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 may have an effect on adjoining programs that weren’t core to their function, and determine expertise enhancements throughout the scope they’d already outlined.
In early inside utilization, the evaluator has already been utilized by dozens of PMs throughout Uber.
The instrument’s worth exhibits up when PMs can deliver 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 Realized
A couple of classes stood out as we constructed and examined the evaluator:
- Frameworks beat generic critique. Broad feedback hardly ever assist groups transfer sooner. The leverage comes from a framework tied to precise determination standards and failure modes.
- Context issues as a lot as language high quality. Many essential alerts reside outdoors the PRD itself, and richer context typically reveals a special set of blind spots than the doc alone.
- Exhausting boundaries make output extra sincere. Defining a small set of important gaps helped the evaluator keep away from calling a PRD review-ready when the basics had been lacking.
- Prioritization is a part of the product. A evaluate instrument that flags every little thing as essential isn’t serving to. The evaluator’s worth comes from telling PMs what to repair first.
- The most effective AI output improves human conversations. The strongest signal the evaluator was working was that later evaluate discussions turned sharper and sooner.
The place Human Judgment Nonetheless Issues
The evaluator doesn’t purpose to make last handbook approval choices or change area specialists. The instrument is most helpful when it strengthens the artifact earlier than professional evaluate.
The toughest a part of product improvement is getting the fitting individuals to make the fitting choices on the proper time, utilizing an artifact robust sufficient to help these choices.
Most product organizations have some equal of checkpoints, evaluate boards, or gated approvals. The names differ, however the problem is identical: how do you be sure the artifact getting into the method is powerful sufficient for the method to do actual work?
AI has actual leverage right here as a structured thought associate that expands context, surfaces blind spots, and sharpens judgment earlier than a call reaches a high-cost discussion board. That’s the reason we constructed the PRD Evaluator. And primarily based on what we’ve seen to this point, we predict this sample (AI that strengthens the enter to human decision-making) will matter nicely past one firm or one instrument.
Acknowledgments
Cowl Photograph Attribution: Created by Gemini
Scorecard Pictures Attribution: Created by Claude
