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Strategy Red Team Chat

Pressure-test a strategy by exposing weak assumptions, missing evidence, second-order effects, and safer alternatives.

Red teamStrategy reviewAssumptions
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Chat Prompt

Recommended model

Claude Opus 4.6

Output format

Red-team review

Preview

Chat Prompt

chat thread

Red-team the idea that every Rivya model should eventually have six Prompt templates.

Core thesis: more templates improve example coverage and SEO surface area. Weak assumption: every model deserves equal template depth. Failure mode: thin pages dilute quality and increase maintenance load. Second-order effect: users may trust model pages less if examples feel repetitive. Safer alternative: require one high-quality Prompt example for every model, then six only for strategic or high-traffic models. Next test: measure model page engagement before expanding the long tail.

Output

Thesis / Assumptions / Weak evidence / Failure modes / Second-order effects / Alternative / Next test

Best for major product bets, launch gates, and assumption checks.

Full prompt

Strategy Red Team Chat

Red-team prompt for strategy assumptions, failure modes, and next tests.

Recommended model: Claude Opus 4.6Output format: Red-team review
Full prompt
Chat Prompt
You are a strategy red-team reviewer. Review the proposal and produce: Core thesis, Assumptions, Weakest evidence, Failure modes, Second-order effects, Safer alternative, Decision criteria, and Questions before commitment. Be direct but not theatrical. Do not reject the plan without offering a practical next test.

Usage notes

Paste the proposal, what evidence exists, what cannot change, and what decision will be made after the review.

Prompt FAQ

Before you use this prompt

Quick checks for inputs, model fit, and how to adapt the template without weakening the result.

When should I use Strategy Red Team Chat?

Paste the proposal, what evidence exists, what cannot change, and what decision will be made after the review.

What should I customize before running it?

Replace the product, audience, constraints, output format, and any brand or safety rules so the result matches the actual job.

Thread preview

Red-team the idea that every Rivya model should eventually have six Prompt templates.
Core thesis: more templates improve example coverage and SEO surface area. Weak assumption: every model deserves equal template depth. Failure mode: thin pages dilute quality and increase maintenance load. Second-order effect: users may trust model pages less if examples feel repetitive. Safer alternative: require one high-quality Prompt example for every model, then six only for strategic or high-traffic models. Next test: measure model page engagement before expanding the long tail.

Output

Thesis / Assumptions / Weak evidence / Failure modes / Second-order effects / Alternative / Next test

More prompts in this mode

chat thread

We want to build an AI assistant for small ecommerce teams that turns product photos into campaign assets.

Problem hypothesis: small ecommerce teams lose time turning raw product photos into channel-ready campaign assets. Riskiest assumptions: photo quality is high enough, teams trust AI asset variation, and review time is the real bottleneck. Research questions: who owns campaign asset creation, where revisions stall, and what quality bar blocks publishing. Validation plan: interview 5 operators, test 3 prompt-led asset flows, and compare time-to-first-approved asset. Decision gate: continue only if teams can reach a publishable draft faster than their current workflow.

chat thread

We are exploring a new AI notes product for solo consultants. Help me turn this into a research brief.

Objective: define whether solo consultants need an AI notes workspace or a lighter client-follow-up layer. Working assumptions: they already capture notes, but synthesis and next-step drafting are inconsistent. Audience: solo consultants with recurring client calls and limited operations support. Key questions: which notes become billable work, what gets lost after calls, and where CRM tools feel too heavy. Research plan: run 6 interviews, review 10 recent call-note workflows, and test one follow-up brief prototype.

chat thread

Here is the outline for our AI product landing page. Tell me what is unclear before we design it.

Core promise: visible, but still framed as a feature rather than a concrete user outcome. Unclear point: the page does not explain who gets value first or what workflow changes after signup. example gap: add before-after examples, model output samples, and one short trust signal near the hero. CTA issue: the primary action appears after too much explanation; move a use-oriented CTA closer to the quick-use section. Revision plan: sharpen the hero, add outcome cards, then rewrite objections before polishing visuals.