Filters applied to ingredients and recipes in the system originate from three distinct sources: Artificial Intelligence (AI), direct System calculations, and User contributions. Understanding where a filter comes from can help you interpret search results more effectively and know the nature of the applied criteria.
1. AI-Generated Filters
How it works: The system employs AI to automatically analyze vast numbers of ingredients and recipes, applying relevant labels based on its interpretation.
Purpose: This enables efficient, large-scale filtering across potentially millions of items, making it easier to find things based on common attributes.
Examples:
Ingredients: Food Type (e.g., Fruit, Vegetables, Meat, Grains, Dairy), Health & Allergy labels (e.g., Vegan, Vegetarian, Celery-Free, FODMAP, Pork-Free).
Recipes: Health & Allergy labels, suggested Meal Type (e.g., Breakfast, Lunch, Dinner, Snack).
Consideration: While considerable effort ensures accuracy, AI analysis across huge datasets can occasionally result in errors or discrepancies. It's always wise to double-check information critical to your needs (especially allergies).
2. System-Generated Filters
How it works: These filters are calculated and applied automatically by the system based on known, quantifiable data inherent to the item, particularly recipes.
Purpose: To provide objective filtering based on factual data derived directly from the item's composition.
Examples (Primarily for Recipes):
Calories per serve
Protein, Carbohydrate, Fat per serve
Number of ingredients
Consideration: The accuracy of these filters depends directly on the accuracy of the underlying data entered for the recipe or ingredient.
3. User-Created Filters
How it works: Users can add custom labels or tags when creating recipes (and potentially ingredients) to provide more personal context, categorization, or subjective attributes.
Purpose: To allow for more nuanced searching based on preferences, suitability, or specific use cases not automatically captured by AI or system calculations.
Examples:
Season (e.g., Summer recipes, Winter recipes)
Occasion (e.g., Holiday, Party food)
Budget (e.g., Budget-friendly, Mid-range – typically a relative scale, not a specific currency value)
Cuisine Type
Popularity / Trends (e.g., Kid-Friendly, Trending)
Consideration: These filters reflect the perspective and context of the user who created them and are therefore more subjective in nature.
Why Understanding the Source Matters
Knowing whether a filter was applied by AI (generally accurate but worth verifying for critical needs), the System (based on factual calculation), or a User (subjective/contextual) helps you better evaluate search results and understand how recipes or ingredients meet your specific requirements.