Last updated 15 April 2026 · 6 min read
How to Write an Evidence-Backed PRD
TL;DR
An evidence-backed PRD has four layers. The bet. The supporting evidence. The challenging evidence. The explicit gaps. Every claim links to source. Synchronise AI generates this structure automatically.
Why most PRDs are broken
Most PRDs are written as if confident.
They state the problem. Propose a solution. List scope.
They rarely show their work.
Six months later nobody can reconstruct why the bet was made.
An evidence-backed PRD inverts that.
The artefact is downstream of the evidence. If the evidence shifts, the PRD knows.
The four layers
- Layer 1 — The bet. One paragraph. Plain language.
- Layer 2 — Supporting evidence. Every event, ticket cluster, interview quote, and behavioural signal that points toward the bet.
- Layer 3 — Challenging evidence. Counter-signal that argues against the bet, listed honestly.
- Layer 4 — Explicit gaps. The evidence you do not yet have and what would close it.
How Synchronise AI generates it
As the Cursor for Product Managers, Synchronise AI reads your live signal and generates each layer with claim-level references back to source.
Layer 2 quotes link to the Intercom ticket.
Layer 3 challenges link to the PostHog cohort that argues the other way.
Layer 4 names the missing study.
The PRD becomes a living object.
When the signal moves, the PRD flags as stale and offers a regenerated version.
A template you can copy today
- Bet — one paragraph.
- Why we believe it — 3–5 evidence cards, each with source.
- Why we might be wrong — 2–3 challenges, each with source.
- Gaps — unknowns and how we would close them.
- Scope and PBIs — inherits the same evidence chain.
Questions
- Does Synchronise AI replace the PM?
- No. It removes the artefact tax so PMs spend more time on judgement and less on copy-paste. The bet is still yours.
Sources
Related guides
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ChatGPT MCPs vs Purpose-Built PM Tools
When ChatGPT with MCPs wins, when purpose-built PM tools win, and why persistent product decisions need state and audit trails.
Why Product Decisions Silently Expire
A taxonomy of how product decisions go stale as data drifts, definitions shift, customers leave, and the original evidence expires.
Synchronise AI is the agentic OS for product teams: a product operating layer that turns customer signal into evidence-backed decisions, PRDs, PBIs, briefs, and GTM artefacts.
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