Most product organizations have some model of a evaluation 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 cut back danger. In observe, it usually reveals a more durable actuality: PMs may be making selections in methods the place the related context extends far past what anyone individual can simply assemble on their very own.
A PRD may attain the evaluation stage with an unsupported headroom assumption, a blind spot in how the function may have an effect on adjoining methods, an unexamined second-order impact, or a policy-sensitive change with out the guardrails reviewers anticipate. In different instances, 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 evaluation 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 would have been surfaced earlier, and makes suggestions inconsistent.
The actual downside isn’t that PMs lack rigor. It’s that product work usually requires a 360-degree view that’s tough to assemble manually within the second: adjoining impacts, accomplice considerations, prior experiments, hidden dependencies, and the questions senior reviewers are prone to ask.
That was the issue we got down to clear up.
Why This Issues at Uber
At Uber, product improvement runs by means of a structured checkpoint course of that offers 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 necessary questions earlier. Moderately 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 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 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 accomplished. It makes use of that context to return a structured evaluation of launch readiness.
Its function is intentionally centered: strengthen the PRD earlier than it reaches high-cost evaluation boards. To not substitute 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 a number of issues earlier and higher:
- Determine an important 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 evaluation 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 could be well-written and nonetheless miss the context, framing, or resolution logic that determines whether or not it’ll maintain up in evaluation.
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 effectively: 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 evaluation for UX parity or discoverability adjustments
- Average evaluation for incremental workflow adjustments or inner tooling migrations
- Full evaluation for net-new capabilities
- Full evaluation with specialised scrutiny for coverage, pricing, or market adjustments
3. Assess Launch Readiness Throughout A number of Dimensions
The evaluation 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 Impression: Does the expertise work effectively throughout consumer segments, geos and potential edge instances?
- Metric and Knowledge Rigor: Does the PRD outline success, guardrails, and a reputable validation method?
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 an important 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 break up into vital 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 evaluation dialog larger sign.
Determine 2: Abstract of the PRD Reviewer output format.
Determine 3: Illustrative scorecard instance.
The place the Worth Exhibits up for PMs
The largest worth is that it adjustments the standard and timing of product pondering.
It Expands a PM’s Discipline of View
Most of the hardest product errors come from incomplete visibility. A PM could not know {that a} comparable speculation was examined earlier by one other staff. They could not understand 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 rapid product floor.
A really helpful evaluator expands that subject of view. It might 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 rely on another person remembering them in a gathering. It might additionally floor context that was by no means explicitly linked within the PRD however remains to be related to understanding the choice.
It Makes Self-Evaluation 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 analysis extra specific. As a substitute 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 methods, 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 quicker 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 “assume by means of draw back danger”. The evaluator is most helpful when it converts critique into revision steerage: outline the baseline, title the goal, add the guardrail, scope the primary launch extra narrowly, acknowledge the chance, or make the dependency specific.
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 methods that weren’t core to their function, and establish expertise enhancements throughout the scope they’d already outlined.
In early inner utilization, the evaluator has already been utilized by dozens of PMs throughout Uber.
The instrument’s worth exhibits up when PMs can convey it into their regular drafting and evaluation workflow, strengthen the constancy of what enters evaluation, and assist reviewers concentrate on higher-signal questions.
What We Realized
A number of classes stood out as we constructed and examined the evaluator:
- Frameworks beat generic critique. Broad feedback hardly ever assist groups transfer quicker. The leverage comes from a framework tied to precise resolution standards and failure modes.
- Context issues as a lot as language high quality. Many necessary alerts reside outdoors the PRD itself, and richer context usually reveals a distinct set of blind spots than the doc alone.
- Laborious 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 had been lacking.
- Prioritization is a part of the product. A evaluation instrument that flags the whole lot as necessary 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 evaluation discussions grew to become sharper and quicker.
The place Human Judgment Nonetheless Issues
The evaluator doesn’t goal to make remaining handbook approval selections or substitute area consultants. The instrument is most helpful when it strengthens the artifact earlier than professional evaluation.
The toughest a part of product improvement is getting the appropriate individuals to make the appropriate selections on the proper time, utilizing an artifact sturdy sufficient to help these selections.
Most product organizations have some equal of checkpoints, evaluation 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 robust 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 call reaches a high-cost discussion board. That’s the reason we constructed the PRD Evaluator. And primarily based on what we’ve seen up to now, we predict this sample (AI that strengthens the enter to human decision-making) will matter effectively past one firm or one instrument.
Acknowledgments
Cowl Photograph Attribution: Created by Gemini
Scorecard Pictures Attribution: Created by Claude