AI Command Patterns & Context Guide

Common User Interaction Patterns

Food Logging Commands

User Intent: Log food intake Common Phrases:

  • "I ate 2 slices of pizza"
  • "Add 1 cup of rice to lunch"
  • "Log breakfast: 2 eggs and toast"
  • "I had dominos chicken pizza"

Expected AI Response:

  1. Extract food items with quantities
  2. Determine meal type (breakfast/lunch/dinner/snacks)
  3. Get nutrition data from database or AI knowledge
  4. Present confirmation dialog with nutrition summary
  5. Add to food diary upon confirmation

Context Date Extraction:

  • "today" → current date
  • "yesterday" → previous date
  • "last Sunday" → specific past date
  • No date mentioned → assume today

Measurement Logging Commands

User Intent: Record body measurements Common Phrases:

  • "My weight is 70kg today"
  • "Waist measurement: 32 inches"
  • "I weighed 154 pounds this morning"

Expected AI Response:

  1. Extract measurement type and value
  2. Convert units if necessary
  3. Save to appropriate measurement table
  4. Provide confirmation with trend information

Goal Setting Commands

User Intent: Update nutrition or fitness goals Common Phrases:

  • "Set my calorie goal to 1800"
  • "I want to consume 120g protein daily"
  • "Change my water goal to 10 glasses"

Expected AI Response:

  1. Identify goal type and target value
  2. Update user_goals table
  3. Apply to future dates using goal timeline function
  4. Confirm changes with previous vs new goals

Progress Inquiry Commands

User Intent: Get progress information Common Phrases:

  • "How am I doing today?"
  • "Show my calorie progress"
  • "What's my weight trend this week?"

Expected AI Response:

  1. Query relevant data based on request type
  2. Calculate progress percentages
  3. Provide trend analysis
  4. Suggest improvements if applicable

Database Context for AI Operations

Food Operations

Tables: foods, food_entries, food_variants Key Operations:

  • Search foods by name/brand
  • Create custom foods when not found
  • Calculate nutrition based on quantity
  • Handle different serving units

Nutrition Calculation:

final_nutrition = (food_nutrition / food_serving_size) * user_quantity

Measurement Operations

Tables: check_in_measurements, custom_measurements, custom_categories Key Operations:

  • Record standard measurements (weight, waist, etc.)
  • Handle custom measurement categories
  • Convert between units (kg/lbs, cm/inches)
  • Track trends over time

Goal Operations

Tables: user_goals Key Operations:

  • Retrieve current goals for date
  • Update goals with timeline management
  • Handle historical vs future goal changes
  • Calculate progress percentages

Family Access Context

Tables: family_access Permission Checks:

  • Always check can_access_user_data() before operations
  • Respect permission levels (read vs write)
  • Handle permission inheritance rules

AI Response Templates

Food Logging Success

"Great! I've analyzed your [meal_type] and found:

**[quantity] [food_name]:**
• [calories] calories
• [protein]g protein, [carbs]g carbs, [fat]g fat
• [fiber]g fiber, [sodium]mg sodium

Would you like me to add this to your [meal_type] for [date]?"

Progress Summary

"Here's your progress for today:

**Calories:** [consumed]/[goal] ([percentage]%)
**Protein:** [consumed]g/[goal]g ([percentage]%)
**Carbs:** [consumed]g/[goal]g ([percentage]%)
**Fat:** [consumed]g/[goal]g ([percentage]%)

[motivational message based on progress]"

Measurement Confirmation

"Recorded your [measurement_type]: [value] [unit]

[Trend information if available]
[Encouragement or suggestions]"

Error Handling Patterns

Food Not Found

  1. Search for similar foods in database
  2. Offer to create custom food
  3. Ask for more specific information (brand, preparation)
  4. Provide nutrition estimation if possible

Invalid Measurements

  1. Check for reasonable ranges
  2. Confirm unusual values with user
  3. Suggest unit conversion if needed
  4. Provide context about normal ranges

Permission Denied

  1. Explain family access limitations
  2. Suggest contacting data owner
  3. Offer alternative accessible features
  4. Maintain privacy without revealing restricted data

Context Optimization

Essential Context (Always Load)

  • User's current goals
  • Today's food entries
  • Active family access permissions
  • Basic app navigation structure

On-Demand Context (Load Based on Query)

  • Food queries: Food database, nutrition facts, meal history
  • Measurement queries: Historical measurements, trends, goals
  • Report queries: Analytics data, progress calculations
  • Settings queries: User preferences, AI configuration

Performance Considerations

  • Cache frequently accessed nutrition data
  • Limit historical data queries to reasonable ranges
  • Use database functions for complex calculations
  • Batch related operations when possible

Integration Points

Direct Database Operations

  • Food entries creation/modification
  • Measurement logging
  • Goal updates
  • Custom food creation

UI Refresh Triggers

  • Dispatch 'foodDiaryRefresh' event after food logging
  • Update measurement charts after new entries
  • Refresh progress bars after goal changes
  • Update family access status after permission changes

Notification Patterns

  • Success toasts for completed operations
  • Error alerts for failed operations
  • Confirmation dialogs for destructive actions
  • Progress notifications for long operations
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