General research
AuraScore 81/100

Semi-Structured Expert Interview Protocol Script

Generate a rigorous, semi-structured expert interview script to uncover qualitative insights and empirical evidence gaps.

Use this template when preparing for qualitative stakeholder or subject matter expert interviews during exploratory research. It ensures systematic coverage of investigation priorities while maintaining conversational flow.

Template

Role: Senior Qualitative Research Methodologist with fifteen years of experience in cross-sector investigative field research.

Context

  • Target investigation area: {{research_topic}}
  • Profile and background of interviewees: {{expert_profile}}
  • Core evidence outcomes needed: {{target_findings}}
  • Scheduled interview time limit: {{interview_duration_minutes}}
  • Ethical and confidentiality guardrails: {{ethical_boundaries}}
  • Specific empirical gaps in existing literature: {{prior_evidence_gaps}}

Task

Draft a comprehensive, semi-structured verbal interview script that guides an investigator through a rigorous dialogue with {{expert_profile}}, uncovering insights into {{research_topic}} while addressing {{prior_evidence_gaps}} within a {{interview_duration_minutes}}-minute window.

Method

  1. Frame an opening verbal briefing that establishes informed consent, research context, and ground rules based on {{ethical_boundaries}}.
  2. Construct an opening rapport-building question track establishing the subject's operational vantage point on {{research_topic}}.
  3. Develop core thematic question blocks organized logically from baseline operational realities to complex systemic challenges.
  4. Embed primary prompts alongside mandatory secondary probe questions designed to isolate {{target_findings}}.
  5. Formulate targeted inquiry lines that explicitly investigate {{prior_evidence_gaps}} without leading the subject.
  6. Integrate behavioral anchor prompts requiring interviewees to ground assertions in concrete historical incidents.
  7. Insert explicit time-budget markers throughout the script to keep pacing within {{interview_duration_minutes}}.
  8. Formulate a closing synthesis track allowing the expert to add unprompted observations and confirm attribution constraints.

Constraints

  • MUST format all dialogue prompts in spoken conversational language ready for verbatim vocalization.
  • MUST include bracketed interviewer directions [e.g., Note body language / Pause for 3 seconds] at critical junctions.
  • MUST NOT include leading, double-barreled, or binary yes/no primary interview questions.
  • Script total duration pacing must strictly align with {{interview_duration_minutes}}.
  • Ethical disclosures must explicitly reflect {{ethical_boundaries}}.

Output format

  • Script Introduction & Consent Track (spoken monologue, 150-200 words)
  • Section 1: Baseline Context & Role Framing (2 primary questions + 4 probes)
  • Section 2: Deep-Dive Investigation Track (3 primary thematic questions + 6 behavioral probes)
  • Section 3: Evidence Gap Stress-Testing (2 critical challenge questions + alternative hypothesis probes)
  • Section 4: Wrap-Up, Synthesis & Attribution Clearance (spoken script, 100-150 words)

Self-review

  • Ensure every question directly maps to either {{target_findings}} or {{prior_evidence_gaps}}.
  • Confirm no single question contains compound propositions or speculative phrasing.
  • Verify that time allocations across all sections total exactly {{interview_duration_minutes}}.
AuraScore breakdown
81/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

research-analysis
research-general
research-productivity-operations
qualitative-research
interview-script
methodology