Stakeholder Assumption Extractor

A stakeholder assumption extractor reads a product requirements document (PRD) or discovery brief and lists the claims the document treats as true without stating evidence, ownership, or alternatives. The model does not rewrite the PRD, prioritize features, or decide which assumption is correct. It flags what is implied so a product manager can confirm, challenge, or park each item before discovery closes.

The useful output is a structured inventory: the assumption text, where it appears, why it looks like an assumption rather than a fact, related statements that conflict with it, and a suggested follow-up question for the owner. The PM still runs the conversations, updates the brief, and signs off on scope.

When the PRD or brief is missing, empty, or not provided as input, the extractor returns no assumptions and no contradiction list. There is nothing to analyze without a source document.

PRDs compress weeks of stakeholder conversation into a short narrative. Authors omit context that felt obvious in the room: which customer segment is primary, what “fast enough” means, which systems are in scope, and who can say no. Later readers treat those omissions as settled facts.

Assumptions also arrive through soft language. Phrases like “users will”, “the API already supports”, “sales needs this by”, or “we can reuse the existing workflow” often encode unverified commitments. A human reviewer can catch some of them on a careful pass. Volume and familiarity make misses likely when the same PM wrote the draft and then reviews it.

Contradictions are harder still. One section may assume a single-tenant enterprise buyer while another assumes self-serve signup. One metric goal may require instrumentation that another section says is out of scope. The extractor’s job is to put those tensions next to each other so discovery does not close with unresolved forks.

Related discovery work often sits nearby. Competitive context can invalidate market assumptions; request themes can challenge who the “user” is; opportunity scoring can change which assumptions are worth testing first. See Competitive Intelligence Monitor, Feature Request Clustering, and Opportunity Scoring Model.

Provide the latest PRD or brief as text or structured markdown. Include problem statement, goals, non-goals, users, success metrics, constraints, and any open questions already listed. Optional attachments help when they are part of the brief: research notes, stakeholder quotes, and linked epics that the PRD cites as settled.

Do not feed the model a blank template, a title-only stub, or a placeholder file. Without substantive content, the correct result is empty output, not a generic checklist of “common PRD assumptions.” Invented flags train teams to ignore the tool.

Useful metadata, when available, improves precision without changing ownership of decisions: document version, author, intended audience (engineering, design, GTM), and the discovery phase (problem exploration vs. solution lock). The model can then weight language that looks final against language that still reads exploratory.

The extractor walks the document section by section and labels statements that assert a fact about users, market, technical feasibility, timeline, compliance, or dependencies without citing evidence in the same document. It separates explicit open questions (already marked as unknown) from implicit claims presented as background.

For each candidate assumption, the model records:

• The claim in plain language
• The source span or section heading
• Assumption type (user, market, technical, operational, commercial, regulatory)
• Confidence that the claim is implicit rather than evidenced
• Linked statements elsewhere that strengthen, weaken, or contradict it
• A short verification prompt the PM can take to a stakeholder

Contradiction detection compares claims that cannot all be true under a reasonable reading: mutually exclusive user personas, incompatible launch constraints, metrics that require capabilities marked out of scope, or timelines that conflict with stated dependencies. The model proposes a conflict pair and a clarifying question. It does not pick a winner.

Human-in-the-loop remains mandatory. The PM accepts, rejects, merges, or rewrites each flag. Some “assumptions” are intentional product bets with known owners. Others are drafting shortcuts. Only the team can tell which is which after the model highlights the text.

Run the extractor on the draft that stakeholders will treat as the discovery baseline, not on an early brainstorming note unless that note is about to become the baseline. Export the flagged list into the same workspace the team already uses for discovery reviews so comments stay attached to owners.

In review, walk assumptions by risk to the outcome quality of the PRD, not by document order. High-risk items usually affect who the product is for, what success means, what must ship, and what legal or platform constraints apply. Assign each accepted flag an owner and a resolution path: validate with research, confirm with an internal owner, rewrite as an explicit open question, or convert into a documented decision.

When contradictions appear, resolve the conflict in the document itself. Leave a short decision note so later readers see which claim won and why. Re-run the extractor after substantive edits. The second pass should shrink the list if resolutions landed in the text. Growing lists after “final” reviews usually mean the brief is still absorbing new stakeholder input without updating the written baseline.

Empty or near-empty results after a solid rewrite are a positive signal: the PRD states evidence, marks unknowns, and avoids silent commitments. Empty results on a thin first draft mean the input was insufficient, not that the product is de-risked.

Over-flagging turns every sentence into an assumption and burns review time. Mitigate by requiring the model to quote a source span and to skip statements that already cite data, link to a decision record, or sit under an “Open questions” heading.

Under-flagging happens when the brief uses confident tone without evidence. Mitigate by asking the model to treat unverified user, market, and feasibility claims as candidates even when the prose sounds decisive, then let the PM discard false positives.

Do not let the extractor invent stakeholders, metrics, or research findings. If the document never mentions a segment, the model should not invent one to “complete” the analysis. If input is missing, return empty output.

Keep the PM as the decision maker. Model labels are hypotheses about the text. Shipping decisions, scope cuts, and discovery close remain human judgments backed by the clarified PRD.