According to God of Prompt on Twitter, top product managers at Google, Meta, and Anthropic use Claude to accelerate core PM workflows with 10 specialized prompts, including PRD drafting, user story generation, competitive teardown, prioritization matrices, roadmap scenario planning, experiment design, stakeholder comms, risk registers, user interview synthesis, and launch checklists. As reported by the original tweet thread, these prompts turn Claude into a structured copilot that reduces PM cycle time on research and documentation by translating unstructured inputs into actionable artifacts. According to the author, the business impact is faster iteration, clearer stakeholder alignment, and higher testing velocity, which creates opportunities for teams to standardize prompt libraries, enforce product quality gates, and scale PM enablement across organizations using Claude.

According to @godofprompt on X, a practical Claude prompt automates Weighted RICE scoring for user stories by collecting Reach per quarter, Impact (0.25–3), Confidence percentage, and Effort in person-months, then ranking stories by RICE score (Reach × Impact × Confidence ÷ Effort). As reported by the original tweet, the prompt also forces reasoning for each input and flags suspiciously low effort estimates, which can reduce delivery risk and improve roadmap alignment. According to the tweet’s embedded instructions, this approach enables faster backlog triage, clearer stakeholder communication, and data-backed prioritization for AI and software teams using Claude in 2026.

According to Andrej Karpathy, a DM POC (Direct Message Point of Contact) in AI companies can significantly streamline communication by allowing team members to directly message high-level decision-makers, thus bypassing traditional product management hierarchies (source: Karpathy, Twitter, Oct 4, 2025). For AI firms, this approach can accelerate decision-making on critical technical issues, improve cross-functional efficiency, and foster innovation by reducing bureaucratic delays. Implementing a DM POC can be especially beneficial in fast-paced AI environments where rapid iteration and quick feedback loops are essential for maintaining a competitive edge.