Comment Question Signal Extractor Framework

This prompt is built for Stage 2: Research and Signal Gathering and focuses on mapper-grade decisions instead of generic brainstorming. It is tuned for content, audience outcomes and returns structured outputs with explicit sequencing, constraints, and measurable checkpoints aligned to Use Comment Question Signal Extractor Framework to drive execution.. Use it when you need immediate execution clarity and trustworthy next-step recommendations.

Use Case

Use when you are running idea audience question mining workflow and need a high-confidence mapper output for stage 2: research and signal gathering with practical detail across content and audience.

Input Variables

Variable	Description	Example
`{{comment_questions}}`	Questions collected from recent post comments	57 questions from 12 posts in the last 2 weeks
`{{recent_content_theme}}`	Main topic your recent posts covered	offer positioning and conversion messaging
`{{target_audience_stage}}`	Awareness level of your audience	problem-aware but solution-unsure

Prompt Template

Variable Inputs

Enter values, then click Apply Values.

Questions collected from recent post comments

Main topic your recent posts covered

Awareness level of your audience

All variables have values and are ready to apply.

Prompt Preview

Original template preview

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

Task: Run a triage workflow for Stage 2: Research and Signal Gathering and produce a decision-ready deliverable that can be executed this week for Comment Question Signal Extractor Framework.

Primary domain signals: content, audience.

Inputs:
- comment questions: {{comment_questions}}
- recent content theme: {{recent_content_theme}}
- target audience stage: {{target_audience_stage}}

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 Comment Question Signal Extractor Framework 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) Build a decision matrix for content and audience from {{comment_questions}}.
2) Weight options by expected lift for {{recent_content_theme}} and {{target_audience_stage}}.
3) Rank top opportunities with confidence levels.
4) Add sequencing and ownership for the first execution sprint.
5) Include one fallback branch for uncertain signals.

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:
- Decision Matrix
- Weighted Rankings
- Recommended Path
- Execution Sequence
- Fallback Branch

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: Research and Signal Gathering Executive Summary

Primary diagnosis: one high-impact bottleneck identified with confidence score 8/10. Action Queue

Immediate fix (24h): scoped correction with owner assignment.
Optimization step (7d): controlled test with threshold gate.
Scale action (30d): rollout with governance and fallback plan. KPI Checkpoint

Baseline: current qualified action rate.
Target: +20 percent within 14 days.
Escalation: trigger rollback if threshold misses two consecutive checks.

Tips

Include at least 40 questions for stronger classification confidence. Re-run monthly to track whether signal mix shifts toward purchase intent.

idea-generationcomment-analysissignal-extractionaudience-questionscontent-ideas