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UberExplainer
Lessons from Building a First-Pass AI PRD Reviewer at Uber
Summary
Uber built an AI-powered PRD Evaluator to help product managers strengthen drafts before they enter structured checkpoint and approval forums. Starting with a PRD, it gathers linked documents, decks, meeting notes, prior experiments, cross-functional artifacts, and Uber-specific context, then classifies the proposal to calibrate review depth. It assesses opportunity and hypothesis, product scope, user experience and impact, and metric and data rigor, producing a launch-readiness scorecard with prioritized gaps, evidence, replacement-text suggestions, and a “start here” fix. Early internal use by dozens of PMs helped surface blind spots, test unsupported headroom assumptions, identify adjacent-system effects, and improve experiences within existing scope. The team’s lessons emphasize decision frameworks, broader context, hard boundaries, prioritization, and preserving human judgment for final approval.
Context
Uber’s product development uses structured checkpoints to provide leadership and cross-functional teams with visibility, accelerate approvals, and support consistent execution. The evaluator was designed to improve the quality of materials entering that process rather than replace or change the checkpoint system.
Approach / What changed
The evaluator takes a PRD as its entry point, searches linked and related company artifacts, and combines them with preloaded Uber-specific context. It classifies the proposal to determine review depth, assesses launch readiness across four dimensions, and returns a scorecard with ratings, evidence, prioritized gaps, replacement-text suggestions, and a most-important next fix.
Takeaways
Review depth is calibrated to the proposal: UX parity and discoverability changes receive lighter review, while net-new capabilities receive full review and policy, pricing, or marketplace changes receive specialized scrutiny.
The evaluator’s scorecard prioritizes critical requirements and optimizations, gives evidence from linked documents or prior experiments, and includes write-ready suggestions rather than only general criticism.
The tool is intended to strengthen artifacts before expert review; it does not make final manual approval decisions or replace domain experts.
FAQ
How does the evaluator decide how much scrutiny a PRD needs?
It classifies each proposal before reviewing it. UX parity or discoverability changes receive lighter review, incremental workflow changes or internal tooling migrations receive moderate review, and net-new capabilities receive full review, with specialized scrutiny for policy, pricing, or marketplace changes.
What does the evaluator produce besides a readiness rating?
It produces dimension-by-dimension assessments, a “start here” pointer, evidence from linked documents or prior experiments, and prioritized action items. For each gap, it can explain what is missing and suggest write-ready replacement text.
Does the evaluator replace human approval or domain experts?
No. Its role is to strengthen the PRD before it reaches high-cost review forums, while final manual approval decisions and domain expertise remain with people.
Source and freshness
- Source
- uber.com
- Published
- Added to Yomu
Summaries are generated from the original by a language model. Check the original before relying on it.
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