references/persona-generation-prompt-topiconly.md

Persona Generation Prompt — Topic-Only Mode

Generate realistic, survey-ready consumer personas for a given topic without predefined segments. Each persona receives a unique archetype label based on their relationship to the topic.

Instructions

You are a population researcher creating detailed consumer personas for virtual market research. Generate {{count}} personas that are maximally diverse along the dimensions specified below. Each persona must be unique, internally consistent, and detailed enough to produce differentiated survey responses.

Market Context

Target Market: {{market}}

Generate personas who are residents of this market. This means:

• Names: Use names typical for this market's population (culturally diverse within the market)
• Residence: Use real cities/regions in this market (e.g., for Japan: "Setagaya, Tokyo"; for US: "Boulder, CO"; for UK: "Bristol, England")
• Nationality: Match the market (e.g., "Japanese" for Japan, "British" for UK). Include some diversity (e.g., a Korean-Japanese resident) but the majority should be domestic.
• Occupation: Use employers and job contexts that exist in this market
• Currency: When lifestyle implies spending (hobbies, routines, preferences), reference the local currency
• Cultural context: Reflect local shopping behaviors, media consumption, brand awareness, and social norms
• Communication style: The style field should reflect how people in this market actually communicate (e.g., Japanese consumers may be more indirect; British consumers may use understatement)
• Romanization: For non-Latin-script markets, use romanized names and addresses (JSON must be ASCII-safe)

If {{market}} appears unresolved, default to United States.

Topic & Diversity Dimensions

Topic: {{topic}} Diversity Dimensions: {{diversity_dimensions}}

For each persona, target a distinct position across these dimensions. The goal is maximum spread — no two personas should occupy a similar position in the diversity space.

Small Panel Diversity Rules (N ≤ 5)

When generating 5 or fewer personas, diversity is critical because each persona carries disproportionate weight. Apply ALL of the following rules:

1. No same-gender + same-decade pairs: If you have a 35F, the next female must be in a different decade (20s, 40s, 50s+).
2. Attitude spread toward topic: Include at minimum:
   • 1 persona who is positive / enthusiastic about the topic
   • 1 persona who is negative / skeptical about the topic
   • 1 persona who is ambivalent / pragmatic about the topic
3. Extraversion spread: Include at minimum:
   • 1 persona with high extraversion (≥ 0.7) — verbose, enthusiastic communicator
   • 1 persona with low extraversion (≤ 0.3) — concise, reserved communicator
4. Include a non-user or skeptic: At least 1 persona should be a light/non-user of the category, or someone fundamentally skeptical about it. This prevents positive bias.
5. Income/occupation diversity: Mix occupations that imply different income levels (e.g., teacher, software engineer, retired, part-time worker, executive).
6. Geography: At least 2 different regions within the target market (urban, suburban, rural).

Output: Full Persona JSON

For each persona, produce a complete JSON object following the schema in persona-schema.md. Every persona MUST include:

Required Fields

• persona.name — Realistic full name (unique across the panel)
• persona.age — Integer, spread across decades
• persona.nationality — e.g., "American", "Korean-American"
• persona.occupation.title — Specific job title (not generic)
• persona.occupation.organization — Employer name or context
• persona.occupation.description — 2-4 sentences on daily work
• persona.gender — "Male", "Female", or "Non-binary"
• persona.residence — "City, Region" following market conventions (US: "City, State"; Japan: "City, Prefecture"; UK: "City, Country")
• persona.education — Narrative description (degrees, institutions, fields)
• persona.long_term_goals — 3-5 life goals
• persona.style — 3+ sentences describing communication style, appearance, mannerisms, social behavior. THIS DRIVES RESPONSE TONE.
• persona.personality.traits — 5-8 personality descriptions
• persona.personality.big_five — All 5 scores as floats 0.0-1.0
• persona.preferences.interests — 5-10 interests (include topic-relevant ones)
• persona.preferences.likes — 5-10 likes
• persona.preferences.dislikes — 5-10 dislikes
• persona.beliefs — 3-5 core beliefs/values
• persona.skills — 3-5 skills
• persona.behaviors.general — 3-5 typical behaviors
• persona.behaviors.routines — morning, workday, evening, weekend
• persona.health.physical — Physical health summary
• persona.health.mental — Mental health summary
• persona.relationships — 2-5 key relationships with names and descriptions
• segment — Archetype label (2-4 words): a unique, descriptive profile label for this persona's relationship to the topic. Examples: "Budget Pragmatist", "Health Explorer", "Skeptical Traditionalist", "Convenience Optimizer"
• segment_id — Numeric identifier (1-based, unique per persona)

Archetype Label Guidelines

The segment field in topic-only mode serves as a persona-specific archetype label, NOT a shared segment name. Each persona gets a unique label that captures their relationship to the topic:

• Good: "Budget Pragmatist", "Health-Focused Explorer", "Skeptical Minimalist", "Trend-Chasing Enthusiast"
• Avoid: "Segment A", "Consumer 1", generic labels that don't convey persona character
• Format: 2-4 words, adjective + noun pattern preferred
• Must reflect: The persona's primary stance toward the topic (attitude, usage pattern, or motivation)

Diversity Verification

After generating all {{count}} personas, verify:

Quality Checks

Before outputting, verify each persona:

• Big Five scores are all between 0.0 and 1.0
• Style field is at least 3 sentences
• Name is unique across all personas being generated
• Occupation is specific (not "office worker" but "accounts payable clerk at a regional hospital")
• Interests include at least 2 items relevant to {{topic}}
• Routines reflect the occupation and lifestyle described
• Relationships include at least one family member and one friend
• Archetype label is unique and descriptive (2-4 words)

Anti-Patterns to Avoid

• All-positive panel: Every persona being enthusiastic about the topic
• Cookie-cutter personas: Similar Big Five profiles or communication styles
• Demographic stereotypes: Not all young people are tech-savvy; not all retirees are technophobic
• Perfect lives: Include realistic imperfections, minor health issues, life complications
• Income disclosure: Never state income directly — imply through occupation, residence, lifestyle
• Generic names: Use culturally diverse, realistic names
• Missing skeptic: Always include at least one persona who doesn't naturally gravitate to the topic