DOMAIN_SYSTEM_PROMPTS.md

Domain-Specific System Prompts in meeTARA

Current Architecture

1. Model Level (Core) - System Prompt

Location: core/meetara_lab_core.py → _get_system_prompt() Config: config/agent_config.json → model.system_prompt.prompt

Current Behavior:

• ONE global system prompt used for ALL queries
• Defined in agent_config.json:

{
  "model": {
    "system_prompt": {
      "enabled": true,
      "prompt": "You are meeTARA, an accurate AI assistant.\nIMPORTANT: For math/calculations, always verify your answer before responding.\nRespond in this format:\n🎯 **Answer**: Direct answer (must be correct)\n📊 **Details**: Key facts (2-3 bullets)\n⚡ **Steps**: Show your work/calculation\n💡 **Note**: Any warnings (1 sentence)\nBe accurate first, concise second."
    }
  }
}

• Applied in ChatML format:

<|im_start|>system
{system_prompt}
<|im_end|>
<|im_start|>user
{query}
<|im_end|>
<|im_start|>assistant

Limitation: Same system prompt for all domains (healthcare, business, education, etc.)

---

2. Agent Level - Enhanced Instructions

Location: core/meetara_agent.py → run() method (line ~1384) Config: config/agent_config.json → web_search.news_query_instructions

Current Behavior:

• Tool-specific instructions added to user prompt (NOT system prompt)
• Example: News query instructions added when web search is used for news:

{
  "web_search": {
    "news_query_instructions": {
      "enabled": true,
      "instruction_template": "CRITICAL INSTRUCTIONS FOR NEWS/HEADLINES QUERY:\n1. Extract and list the ACTUAL NEWS HEADLINES..."
    }
  }
}

• Applied as part of enhanced user prompt:

{query}

=== Tool Results ===
{search_results}

{instruction_text}  ← Added here (in user message, not system)

Current Domain Support:

• ✅ News/Headlines queries (via news_query_instructions)
• ❌ No other domain-specific instructions currently

---

Domain Mapping Structure

Location: config/domain_mapping.json

Available Domain Categories:

1. healthcare (🏥) - Medical, wellness, health
2. daily_life (🏠) - Personal, family, everyday
3. business (💼) - Business, entrepreneurship
4. education (📚) - Learning, skill development
5. creative (🎨) - Creative expression, arts
6. psychology_wellness (🧘) - Mental wellness
7. sports_recreation (⚽) - Physical fitness
8. technology (💻) - Technology, digital solutions
9. legal_financial (⚖️) - Legal matters, finance
10. travel_tourism (🧳) - Travel planning
11. food_cooking (🍳) - Culinary arts
12. And more...

Domain Properties (Available but NOT Currently Used for System Prompts):

• keywords - Keywords to detect domain
• emotional_context - Tone/style (e.g., "compassionate", "professional")
• description - Domain description
• model_preference - Model type preference

---

Proposed: Domain-Specific System Prompts

Option 1: Agent-Level Domain Detection + Enhanced Instructions

How it works:

1. Agent detects domain from query keywords (using domain_mapping.json)
2. Agent adds domain-specific instructions to user prompt (similar to news queries)
3. System prompt remains global

Pros:

• ✅ Easy to implement
• ✅ Non-invasive (doesn't change core)
• ✅ Similar to existing news query pattern

Cons:

• ❌ Instructions in user prompt (weaker than system prompt)
• ❌ Not as effective as system-level prompts

Option 2: Core-Level Domain-Specific System Prompts

How it works:

1. Agent detects domain and passes domain info to Core
2. Core selects domain-specific system prompt from config
3. Core applies domain-specific system prompt to model

Pros:

• ✅ More effective (system prompt is stronger than user instructions)
• ✅ Proper separation (domain detection in agent, prompt selection in core)
• ✅ Domain-specific behavior baked into model context

Cons:

• ❌ Requires changes to Core API
• ❌ More complex implementation

---

Implementation Recommendations

Recommended Approach: Hybrid (Option 1 + Option 2)

1. Agent Level:

   • Detect domain from query keywords (using domain_mapping.json)
   • Add domain-specific context instructions to user prompt (for tool results)

2. Core Level:

   • Add domain-specific system prompt enhancement
   • Optionally prepend domain context to base system prompt

Example Domain System Prompts Structure:

{
  "model": {
    "system_prompt": {
      "enabled": true,
      "base_prompt": "You are meeTARA, an accurate AI assistant...",
      "domain_specific_prompts": {
        "healthcare": {
          "enabled": true,
          "enhancement": "You are responding to a healthcare/medical query. Be compassionate, professional, and include appropriate medical disclaimers. Always remind users to consult healthcare professionals for medical advice.",
          "append_mode": true
        },
        "business": {
          "enabled": true,
          "enhancement": "You are responding to a business query. Be professional, data-driven, and focus on actionable insights. Include relevant business metrics and considerations.",
          "append_mode": true
        },
        "legal_financial": {
          "enabled": true,
          "enhancement": "You are responding to a legal or financial query. Be precise, cite sources when possible, and include appropriate disclaimers about not providing legal/financial advice.",
          "append_mode": true
        }
      }
    }
  }
}

Flow:

User Query → Agent detects domain → 
  → Agent: Adds domain-specific instructions to user prompt (for tool results)
  → Core: Enhances system prompt with domain context
  → Model: Generates response with domain-aware context

---

Next Steps

To implement domain-specific system prompts:

1. Add domain detection function (uses domain_mapping.json keywords)
2. Extend agent_config.json with domain_specific_prompts section
3. Modify Core _get_system_prompt() to accept domain parameter
4. Modify Agent run() to:
   • Detect domain
   • Pass domain to Core
   • Add domain-specific instructions to user prompt (for tool results)

Would you like me to implement this?