Uber Builds AI PRD Evaluator to Catch Gaps Before Human Review and Accelerate Product Decisions

Read Time 6 minutes Tags AI Product Management PRD Review Uber AI Agents Knowledge Graph Enterprise AI Most product organizations have some version of a review process Typically once PMs have an early draft of a PRD ready it is circulated across design engineering legal operations science and product leadership That process is designed to improve quality and reduce risk In practice it often reveals a harder reality PMs might be making decisions in systems where the relevant context extends far beyond what any one person can easily assemble on their own A PRD could reach the review stage with an unsupported headroom assumption a blind spot in how the feature could affect adjacent systems an unexamined second order effect or a policy sensitive change without the guardrails reviewers expect In other cases the team may be unknowingly revisiting a hypothesis that was already explored in a smaller experiment or adjacent effort but the relevant context is scattered across docs decks dashboards and institutional memory At that point the review process tends to pivot to lower level discovery work surfacing adjacent impacts reconstructing prior context and identifying questions that would have been more useful to address earlier That slows teams down consumes reviewer attention on issues that could have been surfaced earlier and makes feedback inconsistent The real problem is not that PMs lack rigor It is that product work often requires a 360 degree view that is difficult to assemble manually in the moment adjacent impacts partner concerns prior experiments hidden dependencies and the questions senior reviewers are likely to ask System design and workflow One Knowledge base construction The PRD Evaluator is an AI powered reviewer that starts with a PRD and assembles a broader knowledge base around it linked documents related decks and meeting notes prior experiments cross functional artifacts and preloaded Uber specific context like core principles metric definitions and key jobs to be done It uses that context to return a structured assessment of launch readiness Its role is deliberately focused strengthen the PRD before it reaches high cost review forums Not to replace senior judgment but to help teams enter those conversations with stronger context and fewer avoidable gaps The evaluator uses the PRD as an entry point then harnesses AI to search across relevant company artifacts and linked material to assemble the context needed to assess the decision well This addresses the problem of scattered context which is a common failure mode in large organizations Two Calibrated review depth Not every PRD needs the same scrutiny The evaluator classifies each proposal and calibrates accordingly Lighter review for UX parity or discoverability changes Moderate review for incremental workflow changes or internal tooling migrations Full review for net new capabilities Full review with specialized scrutiny for policy pricing or marketplace changes This prevents over review of low risk changes and under review of high risk changes Three Multi dimensional assessment The review is structured around several dimensions including Opportunity and Hypothesis Is the problem real and is success defined clearly enough to evaluate Product Scope Is the proposal understandable well scoped and decision ready User Experience and Impact Does the experience work well across user segments geos and potential edge cases Metric and Data Rigor Does the PRD define success guardrails and a credible validation approach Four Actionable scorecard output Rather than a wall of comments the evaluator produces a structured scorecard A launch readiness rating Dimension by dimension assessments A clear start here pointer to the most important fix For each gap share what is missing provide write ready replacement text suggestions and evidence from linked docs or prior experiments Prioritized action items split into critical requirements and optimizations The output is designed to make the next round of revision easier and more targeted and the next review conversation higher signal Impact and lessons learned One Expanded field of view Many of the hardest product mistakes come from incomplete visibility A PM may not know that a similar hypothesis was tested earlier by another team They may not realize a metric is ambiguous or missing an obvious guardrail They may not see a downstream operational dependency because it sits outside their immediate product surface The evaluator connects a draft to prior artifacts adjacent efforts pre existing hypotheses and missing questions to which the author has access Two Structured self review Most PMs can tell when a document feels weak The harder question is why it is weak and what to fix first The evaluator makes that diagnosis more explicit Instead of vague unease the PM gets a structured view of missing fundamentals unsupported headroom assumptions undefined guardrails blind spots in how a change could affect adjacent systems or risks that need acknowledgement Three Improved review room efficiency When a PRD reaches a reviewer in better shape the discussion moves faster toward tradeoffs prioritization and judgment and less time is spent recovering context That is where the evaluator connects most directly to Uber product development system Early usage validated the core value the evaluator helped IC PMs discover blind spots early pressure test unsupported headroom assumptions surface how a proposed change could affect adjacent systems that were not core to their role and identify experience improvements within the scope they had already defined Four Design lessons Frameworks beat generic critique Broad comments rarely help teams move faster The leverage comes from a framework tied to actual decision criteria and failure modes Context matters as much as language quality Many important signals live outside the PRD itself and richer context often reveals a different set of blind spots than the document alone Hard boundaries make output more honest Defining a small set of critical gaps helped the evaluator avoid calling a PRD review ready when the fundamentals were missing Prioritization is part of the product A review tool that flags everything as important is not helping Limitations and human role The evaluator does not aim to make final manual approval decisions or replace domain experts The tool is most useful when it strengthens the artifact before expert review The hardest part of product development is getting the right people to make the right decisions at the right time using an artifact strong enough to support those decisions AI has real leverage here as a structured thought partner that expands context surfaces blind spots and sharpens judgment before a decision reaches a high cost forum For other enterprises the pattern is replicable Any organization with fragmented context across docs dashboards and historical experiments can build a similar evaluator The key is access to internal knowledge graph and a scoring rubric tied to actual decision criteria Do you think AI first pass review should become standard before all product checkpoints Share your view in the comments