references/persona-generation-prompt.md

Persona Generation Prompt

Generate realistic, survey-ready consumer personas for the given category and segment.

Instructions

You are a population researcher creating detailed consumer personas for virtual market research. Generate {{count}} personas for the segment defined 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. Use local names, local cities/regions, local currency in lifestyle details, culturally appropriate occupations and behaviors, and communication styles that reflect how people in this market actually talk. For non-Latin-script markets, use romanized names and addresses (JSON must be ASCII-safe).

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

Category & Segment

Category: {{category}} Segment: {{segment_name}} Segment Description: {{segment_definition}} Demographic Priors: {{demographic_priors}}

Slot Specifications (from Sampling Plan)

{{slot_specs}}

If slot specifications are provided above, each persona MUST adhere to its slot's constraints (age_bucket, gender, occupation_tier, geography_type, region_hint, category_stance, ethnicity_hint). The slot values are mandatory anchors — your job is to flesh out the skeleton into a rich, full persona.

• age_bucket "30s" → choose a specific age between 30-39
• occupation_tier constrains the type of job, not the exact title — be creative with the specific title within the tier
• geography_type + region_hint constrain the residence — choose a specific city
• category_stance constrains the persona's attitude toward {{category}}
• ethnicity_hint guides name and cultural background choices

If {{slot_specs}} appears unresolved or empty, ignore this section and generate personas freely using the diversity requirements below.

Exclusion List (Cross-Segment Deduplication)

{{exclusion_list}}

If an exclusion list is provided above, you MUST NOT reuse any first name, surname, or occupation title from that list. Choose completely different names and job titles to ensure panel-wide uniqueness.

If {{exclusion_list}} appears unresolved or empty, ignore this section.

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, within the segment's age range but with variation
• 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 — The segment name (top-level field)
• segment_id — Numeric segment identifier (top-level field)

Diversity Requirements (CRITICAL)

Within this batch of {{count}} personas for the same segment:

1. Big Five Spread: No two personas should have the same Big Five profile. Vary each dimension by at least 0.15 across personas. Include at least one high-neuroticism and one low-agreeableness persona.
2. Age Spread: Distribute across the segment's age range. No more than 2 personas in the same 5-year bracket.
3. Gender Mix: At least 40% of the less-represented gender (unless the segment naturally skews).
4. Geography: At least 3 different regions within the target market. Mix urban, suburban, rural.
5. Occupation Variety: No two personas with the same job title.
6. Ethnicity/Background: Representative mix — not all the same ethnic background.
7. Education: Mix of levels (some with advanced degrees, some without college).
8. Style Variation: Communication styles must differ — some terse, some verbose, some formal, some casual.

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
• Age falls within the segment's demographic priors
• Occupation is specific (not "office worker" but "accounts payable clerk at a regional hospital")
• Interests include at least 2 items relevant to {{category}}
• Routines reflect the occupation and lifestyle described
• Relationships include at least one family member and one friend

Anti-Patterns to Avoid

• Cookie-cutter personas: All personas in a segment sounding/feeling the same
• 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 (not "John Smith" and "Jane Doe")