# Prompt Management System

A centralized system for managing LLM prompts with templating, structured outputs, and easy editing capabilities.

## 🎯 System Goals

- **Centralized Management**: All prompts in one organized location
- **Easy Editing**: Modify prompts without touching code
- **Template Support**: Dynamic variable substitution
- **Structured Outputs**: JSON schemas for reliable responses
- **Type Safety**: Validation of required variables

## 📁 File Organization

```
living_agents/prompts/
├── react_to_situation.md           # Character response generation
├── score_observation_importance.md # Observation memory scoring
├── score_reflection_importance.md  # Reflection memory scoring
├── score_plan_importance.md        # Plan memory scoring
├── extract_character_from_memories.md   # Character data extraction
├── extract_character_from_memories.json # Character data schema
├── generate_reflection.md          # Reflection generation prompt
├── generate_reflection.json        # Reflection schema
├── assess_trait_impact.md          # Trait analysis prompt
├── assess_trait_impact.json        # Trait update schema
└── character_summary.md            # Character summary generation
```

## 📝 Prompt Templates

### Template Syntax

Use `{{variable_name}}` for dynamic substitution:

```markdown
You are {{character_name}}.
Age: {{character_age}}
Personality: {{character_personality}}

Relevant memories:
{{memory_context}}

Current situation: {{situation}}

Respond as {{character_name}} in first person past tense.
```

### Variable Extraction

System automatically detects required variables:

- Parses `{{variable}}` patterns
- Validates all variables are provided
- Warns about missing or extra variables
- Ensures template completeness

## 🏗️ JSON Schemas

### Structured Output Support

Pair `.md` prompts with `.json` schemas for reliable structured responses:

**Example Schema** (assess_trait_impact.json):

```json
{
  "type": "object",
  "properties": {
    "trait_updates": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "trait_name": {
            "type": "string",
            "pattern": "^[a-zA-Z]+$"
          },
          "action": {
            "type": "string",
            "enum": ["create", "strengthen", "weaken"]
          },
          "new_strength": {
            "type": "integer",
            "minimum": 1,
            "maximum": 10
          },
          "description": {
            "type": "string"
          },
          "reasoning": {
            "type": "string"
          }
        }
      }
    }
  }
}
```

### Schema Benefits

- **Guaranteed Structure**: Always get expected JSON format
- **Type Validation**: Ensures correct data types
- **Field Requirements**: Specify required vs optional fields
- **Value Constraints**: Set min/max values, string lengths
- **Consistent Parsing**: No more JSON parsing errors

## 🔧 API Usage

### Basic Prompt Retrieval

```python
from living_agents.prompt_manager import PromptManager

# Simple template substitution
prompt = PromptManager.get_prompt('react_to_situation', {
    'character_name': 'Alice',
    'character_age': 23,
    'situation': 'Someone asks how you are feeling'
})
```

### Structured Output

```python
from living_agents.prompt_manager import PromptManager

# Get both prompt and schema
prompt, schema = PromptManager.get_prompt_with_schema('assess_trait_impact', {
    'observation': 'I felt nervous talking to Emma',
    'traits_summary': 'shy (8/10), romantic (7/10)'
})

# Use with LLM structured output
if schema:
    response = await llm.get_structured_response(messages, schema)
else:
    response = await llm.chat(messages)
```

### Development Helpers

```python
# List all available prompts
prompts = PromptManager.list_prompts()
print(prompts)  # Shows prompt names and required variables

# Get prompt details
info = PromptManager.get_prompt_info('react_to_situation')
print(f"Variables needed: {info['variables']}")

# Reload during development
PromptManager.reload_prompts()  # Refresh from files
```

## 🎯 Prompt Design Guidelines

### Template Best Practices

- **Clear Instructions**: Specify exactly what you want
- **Consistent Formatting**: Use standard variable naming
- **Context Provision**: Give LLM necessary background
- **Output Specification**: Define expected response format
- **Example Inclusion**: Show desired output style

### Schema Design

- **Minimal Required Fields**: Only require truly essential data
- **Reasonable Constraints**: Set realistic min/max values
- **Clear Descriptions**: Help LLM understand field purposes
- **Flexible Structure**: Allow for natural language variation
- **Error Prevention**: Design to minimize parsing failures

## 🔄 System Benefits

### For Developers

- **Easy Maintenance**: Edit prompts without code changes
- **Type Safety**: Automatic variable validation
- **Consistent Structure**: Standardized prompt format
- **Debugging Support**: Clear error messages for missing variables

### For LLM Performance

- **Structured Outputs**: Eliminates JSON parsing errors
- **Consistent Prompting**: Reduces response variance
- **Context Optimization**: Templates ensure complete context
- **Schema Guidance**: Helps LLM generate correct format

This system makes prompt management scalable, maintainable, and reliable across the entire roleplay system.