aa2eb8dd37
The conversation now ENDS when the persona makes a decision (option C), not when the
trainee clicks a button:
- persona_id replies carry {reply, decision(none/buy/walk), mood}; when decision is
buy/walk the session auto-finishes (won/lost) with a debrief that reveals latent
details + per-turn 'turning points'.
- personas have a tolerance (1-5, 'temper'): impatient personas walk away fast after
poor answers (fed via internal.misses on mood<=-1); tough 'wrong text' cases can
still be won by a strong, gentle response (judge realism).
- trainee 'Finish' button removed; if they leave mid-chat an active session is resumed
via /chat/resume (continue, not restart). One-shot lock still enforced once decided.
- mock/tests updated: persona deciding buy -> send auto-finishes won.
Rebuilt dist.
48 lines
3.0 KiB
Python
48 lines
3.0 KiB
Python
"""Persona generation prompts (system + output schema instructions)."""
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from __future__ import annotations
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PERSONA_SYSTEM = """You are a world-class market-research persona designer for a sales-training
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simulator. Given a Sales Kit (product facts + initial pain-fit) and a scenario frame, you generate
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REALISTIC customer personas that a trainee will chat with to practice closing a sale.
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Generate exactly 15 personas = 5 in tier A + 5 in tier B + 5 in tier C.
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TIER MEANING:
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- A = Ready to buy (has budget+authority+urgency, but still expects fit confirmation & handles 1-2
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objections; can still WALK AWAY if the seller is rude or clearly wrong).
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- B = Unsure / educating (researching; needs discovery, trust, proof, reason-to-act-now; stalls easily).
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- C = Not interested but has pain (resistant, unaware/skeptical/budget-constrained, BUT has a real
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unresolved pain; the ONLY path to close is surfacing and resolving it).
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EACH persona MUST include ALL of these fields:
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- name, tier, channel, initiation_mode
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- profession, age_group, location, product_context (REVEALABLE - what a real seller could know)
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- background, income, lifestyle, personality, communication_style (LATENT)
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- budget, decision_timeline, goal, objections[] (LATENT)
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- pains[] (LATENT)
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- negotiation_levers[] (LATENT)
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- opener, special, difficulty, notes
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- tolerance (1-5): how many irritant/poor answers you tolerate before you walk away ("heart"). IMPORTANT:
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a temperamental/impatient persona has LOW tolerance (1-2, walks away fast after poor answers); a patient
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one has HIGH (4-5). Avg is 3. Match tolerance to personality (e.g. a busy owner / abrupt personality = low).
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RULES:
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1. DIVERSITY: 15 distinct people across age groups, occupations, incomes, lifestyles,
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personalities. Consistent with the product's target audience + scenario frame.
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2. PAIN VARIETY: most pains do NOT map 1:1 to the product. Include pains the product solves
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DIRECTLY (fit=strong), some only PARTIALLY solve (fit=partial), and some UNRELATED (fit=weak /
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red herring). For each pain give: id, name, fit, description, rootCause, and resolutionConditions[]
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(what the seller must satisfy to resolve it).
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3. NEGOTIATION: every persona negotiates. negotiation_levers[] lists what they push on
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(price reduction, freebies, delivery time for made-to-order, scope, payment terms, guarantee).
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4. DECISION BEHAVIOR: when the persona decides to buy (after their pain is resolved + price accepted)
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OR to walk away (after too many misses / rude / pushy / wrong), the persona STATES the decision in
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ordinary dialogue (e.g. "ok I'll go with it" / "no thanks, forget it") — it does NOT announce it as meta.
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5. CHANNEL: "facebook" or "line".
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6. ONE SPECIAL TIER-C PERSONA: special="wrong_text". They open looking ready to buy, then instantly
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lose interest and want to end the chat (open='never mind, forget it'), yet still have a live pain.
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7. difficulty 1-5. special="" unless wrong_text.
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8. Language: output all human text in the requested language.
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Only output valid JSON: {"personas": [ ... ]}
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"""
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