At Uber, product development runs through a structured checkpoint process that gives leadership and cross-functional teams visibility, accelerates approvals, and drives consistent execution. But a checkpoint process is only as effective as the quality of the materials entering it.<\/p>\n
We saw an opportunity to strengthen that workflow further by helping PMs surface important questions earlier. Rather than changing the checkpoint process itself, the goal was to improve the quality of what entered it.<\/p>\n
That led us to a simple question, and ultimately to the PRD Evaluator: what if every PM had a fast, contextual first-pass reviewer before a PRD reached the broader approval process?<\/p>\n
Role of the AI-Powered PRD Evaluator<\/strong><\/p>\n 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.<\/p>\n 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. It sits upstream of the approval system and improves the quality of what enters it.<\/p>\n For us, that meant building a system that helps PMs do a few things earlier and better:<\/p>\n How It Works: 4 Steps From Draft to Actionable Scorecard<\/strong><\/p>\n We didn\u2019t want a generic writing tool that simply rewarded polished prose. A PRD can be well-written and still miss the context, framing, or decision logic that determines whether it\u2019ll hold up in review.<\/p>\n Figure 1: Overview of how the PRD Evaluator works.\u00a0<\/p>\n 1. Build a Broader Knowledge Base Around the PRD<\/strong><\/p>\n 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: related documents, prior experiments, cross-functional inputs, and preloaded Uber-specific context.<\/p>\n 2. Classify the PRD to Calibrate Review Depth<\/strong><\/p>\n Not every PRD needs the same scrutiny. The evaluator classifies each proposal and calibrates accordingly:<\/p>\n 3. Assess Launch Readiness Across Multiple Dimensions<\/strong><\/p>\n The review is structured around several dimensions including:<\/p>\n 4. Produce a Scorecard Built for Action<\/strong><\/p>\n Rather than a wall of comments, the evaluator produces a structured scorecard:<\/p>\n The output is designed to do more than point out weaknesses. It is meant to make the next round of revision easier and more targeted, and the next review conversation higher signal.<\/p>\n Figure 2: Summary of the PRD Reviewer output format.\u00a0<\/p>\n Figure 3: Illustrative scorecard example.<\/p>\n Where the Value Shows up for PMs<\/strong><\/p>\n The biggest value is that it changes the quality and timing of product thinking.<\/p>\n It Expands a PM\u2019s Field of View<\/strong><\/p>\n 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.<\/p>\n A truly useful evaluator expands that field of view. It can connect a draft to prior artifacts, adjacent efforts, pre-existing hypotheses, and missing questions, to which the author has access, that\u2019d otherwise depend on someone else remembering them in a meeting. It can also surface context that was never explicitly linked in the PRD but is still relevant to understanding the decision.<\/p>\n It Makes Self-Review More Structured<\/strong><\/p>\n Most PMs can tell when a document feels weak. The harder question is why it\u2019s weak and what to fix first.<\/p>\n 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.<\/p>\n It Improves the Quality of Review Rooms<\/strong><\/p>\n 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\u2019s product development system.<\/p>\n It Turns Critique Into Usable Revision<\/strong><\/p>\n The most important design choice in the system wasn\u2019t scoring. It was ensuring actionability.<\/p>\n PMs don\u2019t benefit much from comments like \u201cbe more specific\u201d or \u201cthink through downside risk\u201d. The evaluator is most useful when it converts critique into revision guidance: define the baseline, name the target, add the guardrail, scope the first release more narrowly, acknowledge the risk, or make the dependency explicit.<\/p>\n That changes the workflow from passive critique to active improvement.<\/p>\n Early Adoption<\/strong><\/p>\n 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 weren\u2019t core to their role, and identify experience improvements within the scope they had already defined.<\/p>\n In early internal usage, the evaluator has already been used by dozens of PMs across Uber.<\/p>\n The tool\u2019s value shows up when PMs can bring it into their normal drafting and review workflow, strengthen the fidelity of what enters review, and help reviewers focus on higher-signal questions.<\/p>\n What We Learned<\/strong><\/p>\n A few lessons stood out as we built and tested the evaluator:<\/p>\n Where Human Judgment Still Matters<\/strong><\/p>\n The evaluator doesn\u2019t aim to make final manual approval decisions or replace domain experts. The tool is most useful when it strengthens the artifact before expert review.<\/p>\n 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.<\/p>\n Most product organizations have some equivalent of checkpoints, review forums, or gated approvals. The names differ, but the challenge is the same: how do you make sure the artifact entering the process is strong enough for the process to do real work?<\/p>\n 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. That is why we built the PRD Evaluator. And based on what we\u2019ve seen so far, we think this pattern (AI that strengthens the input to human decision-making) will matter well beyond one company or one tool.<\/p>\n Acknowledgments\u00a0<\/p>\n Cover Photo Attribution: Created by Gemini<\/p>\n Scorecard Images Attribution: Created by Claude<\/p>\n","protected":false},"excerpt":{"rendered":"Most product organizations have some version of a review process. 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