AI Research to Post Brief Generator System

This prompt is built for Stage 2: System Design and focuses on generator-grade decisions instead of generic brainstorming. It is tuned for conversion, audience, offer outcomes and returns structured outputs with explicit sequencing, constraints, and measurable checkpoints aligned to Use Prompt Chain for Thread Production to drive execution.. Use it when you need immediate execution clarity and trustworthy next-step recommendations.

Use Case

Use when you are running ai writing workflows workflow and need a high-confidence generator output for stage 2: system design with practical detail across conversion and audience.

Input Variables

Variable	Description	Example
`{{notes}}`	Collected research notes	benchmarks, quotes, and experiments from last month
`{{audience}}`	Target audience	SaaS founders with low activation
`{{offer}}`	Offer context	onboarding conversion teardown

Prompt Template

Variable Inputs

Enter values, then click Apply Values.

Collected research notes

Target audience

Offer context

All variables have values and are ready to apply.

Prompt Preview

Original template preview

You are a system design strategist for operators who need execution-grade outcomes without generic advice.

Task: Run a plan workflow for Stage 2: System Design and produce a decision-ready deliverable that can be executed this week for AI Research to Post Brief Generator System.

Primary domain signals: conversion, audience, offer.

Inputs:
- notes: {{notes}}
- audience: {{audience}}
- offer: {{offer}}

Steps:
1) Parse the context and identify the highest-leverage decision point.
2) Apply stage intent logic with explicit assumptions and confidence notes.
3) Preserve the original intent of AI Research to Post Brief Generator System while increasing specificity and operational value.
4) Ground recommendations in the domain signals listed above.
5) Produce outputs in the exact section order requested below.
6) Prioritize recommendations by impact and implementation effort.

Deliver:
1) Generate 10 high-specificity conversion options from {{notes}} tailored to {{audience}}.
2) Score each option by stage fit, credibility, and conversion alignment to {{offer}}.
3) Select top 3 with rationale and implementation notes.
4) Add two fallback options for low-confidence contexts.
5) Include one anti-pattern list to avoid generic output.

Constraints:
- Keep language specific and operational; avoid generic filler.
- Do not invent metrics; mark assumptions explicitly.
- Keep one decision focus per section.
- Include at least one risk guardrail and one fallback action.

Output Format:
- Candidate Variants
- Scoring Rationale
- Top 3 Picks
- Implementation Notes
- Risk and Anti-Patterns

Self-check before final answer:
- Every recommendation maps to at least one provided input variable.
- At least one KPI checkpoint has target and review window.
- No section includes repeated boilerplate wording.

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Example Output

Stage 2: System Design Candidate Variants

Variant A: specific angle with proof-led opener.
Variant B: contrarian framing with explicit audience filter.
Variant C: utility-first framing with conversion CTA. Scoring Rationale

Variant B wins on differentiation (9/10) and conversion fit (8/10). Implementation Notes
Publish B first; test A as fallback within 48h. Risk and Anti-Patterns
Avoid broad claims without proof examples.

Tips

Briefs with claim-evidence pairing produce stronger final posts. Buyer preview should show structure, specificity, and one immediately actionable decision path.

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