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AI-Powered Product Requirement Document (PRD) Evaluation

AI-Powered Product Requirement Document (PRD) Evaluation

4
posts
2026

Uber is enhancing 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).

2026

Lessons from Building a First-Pass AI PRD Reviewer at Uber

5/12/2026

This post details the evolution and lessons learned from building the AI-Powered PRD Evaluator. It describes 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, context over language quality, hard boundaries for honest output, prioritization of action items, and the AI's role in improving human conversations. The post emphasizes that the tool strengthens artifacts before expert review and doesn't replace human judgment.

AI Prototyping Is Changing How We Build Products at Uber

4/15/2026

This post details the adoption and impact of AI-powered prototyping at Uber, showcasing how it accelerates idea exploration, speeds up alignment, and unblocks execution by making ideas tangible earlier in the product lifecycle. It emphasizes the symbiotic relationship between AI prototypes and PRDs, and discusses pitfalls and guardrails for effective AI prototyping, positioning it as a complementary tool to existing product development practices.

Open Source and In-House: How Uber Optimizes LLM Training

4/14/2026

This post details Uber's in-house LLM training infrastructure and pipeline, focusing on the Michelangelo platform. It describes the hardware (A100, H100 GPUs), orchestration (Kubernetes, Ray), and training stack (PyTorch, DeepSpeed, Hugging Face Transformers, NCCL). The distributed training pipeline is explained, covering multi-host/GPU communication, data preparation, model training, and checkpoint management. The post also presents results on training state-of-the-art LLMs and explores throughput/MFU optimization strategies like CPU offload and flash attention, analyzing their impact on different model sizes (Llama 2 70B vs. 7B) and hardware (A100 vs. H100).

How Uber Built an Agentic System to Automate Design Specs in Minutes

3/11/2026

This 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.