PR FAQ

PR FAQ is the working-backwards artifact used at Amazon and AWS: a fictional press release plus frequently asked questions that describe a future product as if it has already launched, forcing the team to clarify customer value before building.

Key points

  • The press-release section answers five core questions: who the customer is, what problem is being solved, what value is created, how the solution works, and why it matters to the customer and business [src-017].
  • The FAQ section anticipates stakeholder questions so teams can align internally before spending build resources [src-017].
  • The document is fictional at first, but it should be specific enough that a reader can understand the customer problem, the proposed experience, and the expected business value [src-017].
  • Sabrina describes the PR FAQ as an early experiment: if the idea does not make sense on paper, it is a signal to refine before building [src-017].
  • The PR FAQ is not the final deliverable; it is a mechanism for building the right thing and moving into prototype/test cycles with less waste [src-017].
  • Carr adds the portfolio-management lesson: PR FAQs should create a product funnel, not a product tunnel. Many ideas can be written and compared, but only the strongest should consume scarce build resources [src-018].
  • Writing a PR FAQ can be piloted inside a single product group, but changing product development at company scale usually needs senior leadership buy-in and disciplined practice [src-018].

Related entities

  • Amazon — origin context for the mechanism
  • Amazon Web Services — teaches the mechanism to customers through Digital Innovation and PACE
  • Bill Carr — explains the mechanism through the Working Backwards book and consulting work

Related concepts

Source references

  • [src-017] AWS Events — “Working Backwards | How to Build Like AWS” (2026-02-24)
  • [src-018] Lenny’s Podcast — “Unpacking Amazon’s unique ways of working | Bill Carr (author of Working Backwards)” (2023-11-02)

Robin Cartier perspective

This page is part of Robin Cartier's working AI knowledge graph: a practical research layer for production AI, recommendation systems, experimentation, GEO, and agentic web readiness.

The useful next step is to connect this concept back to applied product leadership and operating models.

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