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AI-Powered Product Requirement Document (PRD) Evaluation

5 posts · Mar 2026 – May 2026

What this trail covers

Uber has enhanced its AI capabilities and product development lifecycle with advanced tools. Initially, the focus was on an AI system (PRD Evaluator) to act as a first-pass reviewer for Product Requirement Documents (PRDs), improving their quality by assembling context and identifying gaps. This has evolved to encompass AI-powered prototyping, accelerating concept exploration and team alignment. Furthermore, Uber is developing sophisticated in-house LLM training infrastructure (Michelangelo platform) and has launched a company-wide Responsible AI program. This program builds governance directly into the AI development lifecycle through a centralized Model Catalog, automated tooling, feature-importance explainability, early compliance checks, and broad employee education, creating a scalable foundation for responsible AI innovation. The Model Catalog serves as a single source of truth for model metadata, integrated into the ML development process. Explainability is achieved through global (PFI) and local (SHAP, Integrated Gradients) attributions, linked to Model Cards. Governance is embedded into workflows via PRD/ERD reviews and pre-deployment Model Card completion. Education is provided through training courses and an AI Resource Hub. Adoption focuses on operationalizing governance across existing AI/ML assets with life-cycle-aware, adaptive, technology-agnostic controls that balance automation with context and operate at the system level.

All Uber trails
  1. Lessons from Building a First-Pass AI PRD Reviewer at UberThis post details the development and lessons learned from building the AI-Powered PRD Evaluator, a system designed to act as a first-pass reviewer for Product Requirement Documents. It outlines the system's four-step process: building a knowledge base, classifying PRDs, assessing launch readiness across dimensions (Opportunity/Hypothesis, Product Scope, User Experience/Impact, Metric/Data Rigor), and producing an actionable scorecard. Key learnings include the importance of frameworks over generic critique, the value of context beyond the PRD, the benefit of hard boundaries for honest output, the necessity of prioritization, and how AI can improve human conversations. The post also touches on the evolution towards AI-powered prototyping and in-house LLM training infrastructure.
  2. Open Source and In-House: How Uber Optimizes LLM TrainingThis post details Uber's in-house LLM training infrastructure and pipeline, named Michelangelo. It covers the hardware (NVIDIA A100/H100 GPUs), orchestration (Kubernetes, Ray), and training stack (PyTorch, Hugging Face, DeepSpeed, NCCL). The distributed training pipeline is described, including communication, data preparation, model training, and checkpointing. The post also presents results on training state-of-the-art LLMs like Llama 2, focusing on throughput and Model Flops Utilization (MFU) optimization through techniques like DeepSpeed ZeRO-stage-3 CPU Offload and flash attention, and analyzes compute vs. memory bound scenarios for different model sizes.
  3. Under the Hood: Scaling Responsible AI at UberThis post details the evolution and operationalization of Uber's Responsible AI program. Key technical contributions include the development of a centralized Model Catalog with integrated Model Cards, automated population of metadata, and integration into the ML development workflow. Explainability is enhanced through the integration of Permutation Feature Importance (PFI) for global attributions and SHAP/Integrated Gradients for local attributions, directly linked to Model Cards. Governance is operationalized by embedding checks into PRD/ERD reviews and implementing pre-deployment Model Card completion as an auditable gating mechanism. The program emphasizes a shift-left approach, adaptive controls, and system-level visibility for AI governance.
  4. How Uber Built an Agentic System to Automate Design Specs in MinutesThis post introduces uSpec, an agentic system that uses AI agents and Figma Console MCP to automate the generation of design system component specifications directly within Figma. It addresses the enterprise documentation problem by creating accurate, complete, and up-to-date specs for components across multiple implementation stacks, significantly reducing manual effort and improving consistency and speed. This extends the application of AI in product development beyond PRD evaluation and prototyping to the critical area of design system documentation.