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solaire_oa parent
Ok. Tried it, I'm not super impressed.

    Client: Ollama (phi4) - 90164ms. StopReason: stop. Tokens(in/out): 365/396
    ---PROMPT---
    user: Extract from this content:
    Grave Digger: 
     Ingredients
    
    - 1 1/2 ounces vanilla-infused brandy*
    
    - 3/4 ounce coffee liqueur
    
    - 1/2 ounce Grand Marnier
    
    - 1 ounce espresso, freshly brewed
    
    - Garnish: whipped cream
    
    - Garnish: oreo cookies, crushed
    
    Steps
    
    1.  Add all ingredients into a shaker with ice and shake until
        well-chilled.
    
    2.  Strain into a coupe.
    
    3.  Top with whipped cream and crushed Oreo cookies (discarding cream in
        center).
    
    *Vanilla-infused brandy: Cut 2 fresh vanilla pods lengthwise and place
    into a 750 mL bottle of brandy. Let sit for 3 to 5 days, shaking
    occasionally. Strain out pods and store.
    
    
    
    IngredientType
    ----
    - Alcohol
    - Sweetener
    - Sour
    - Aromatic
    - Bittering_agent
    - Food
    - Dilution
    
    LiquorType
    ----
    - Gin
    - Vodka
    - Rum
    - Whiskey
    - Tequila
    - Mezcal
    - Brandy
    - Cognac
    - Liqueur
    - Wine
    - Absinthe
    
    Answer in JSON using this schema:
    {
      Name: string,
      Ingredients: [
        {
          Text: string,
          Type: IngredientType,
          Liquor_type: LiquorType or null,
          Name_brand: string or null,
          Unit_of_measure: string,
          Measurement_or_unit_count: string,
        }
      ],
      Steps: [
        {
          Number: int,
          Instruction: string,
        }
      ],
      Garnishes: string[],
    }
    
    ---LLM REPLY---
    ```json
    {
      "Name": "Grave Digger",
      "Ingredients": [
        {
          "Text": "vanilla-infused brandy*",
          "Type": "Alcohol",
          "Liquor_type": "Brandy",
          "Name_brand": null,
          "Unit_of_measure": "ounces",
          "Measurement_or_unit_count": "1 1/2"
        },
        {
          "Text": "coffee liqueur",
          "Type": "Liqueur",
          "Liquor_type": "Liqueur",
          "Name_brand": null,
          "Unit_of_measure": "ounce",
          "Measurement_or_unit_count": "3/4"
        },
        {
          "Text": "Grand Marnier",
          "Type": "Liqueur",
          "Liquor_type": "Liqueur",
          "Name_brand": "Grand Marnier",
          "Unit_of_measure": "ounce",
          "Measurement_or_unit_count": "1/2"
        },
        {
          "Text": "espresso, freshly brewed",
          "Type": "Bittering_agent",
          "Liquor_type": null,
          "Name_brand": null,
          "Unit_of_measure": "ounces",
          "Measurement_or_unit_count": "1"
        }
      ],
      "Steps": [
        {
          "Number": 1,
          "Instruction": "Add all ingredients into a shaker with ice and shake until well-chilled."
        },
        {
          "Number": 2,
          "Instruction": "Strain into a coupe."
        },
        {
          "Number": 3,
          "Instruction": "Top with whipped cream and crushed Oreo cookies (discarding cream in center)."
        }
      ],
      "Garnishes": [
        "whipped cream",
        "oreo cookies, crushed"
      ]
    }
    ```
    ---Parsed Response (class Recipe)---
    {
      "Name": "Grave Digger",
      "Ingredients": [
        {
          "Text": "vanilla-infused brandy*",
          "Type": "Alcohol",
          "Liquor_type": "Brandy",
          "Name_brand": null,
          "Unit_of_measure": "ounces",
          "Measurement_or_unit_count": "1 1/2"
        },
        {
          "Text": "espresso, freshly brewed",
          "Type": "Bittering_agent",
          "Liquor_type": null,
          "Name_brand": null,
          "Unit_of_measure": "ounces",
          "Measurement_or_unit_count": "1"
        }
      ],
      "Steps": [
        {
          "Number": 1,
          "Instruction": "Add all ingredients into a shaker with ice and shake until well-chilled."
        },
        {
          "Number": 2,
          "Instruction": "Strain into a coupe."
        },
        {
          "Number": 3,
          "Instruction": "Top with whipped cream and crushed Oreo cookies (discarding cream in center)."
        }
      ],
      "Garnishes": [
        "whipped cream",
        "oreo cookies, crushed"
      ]
    }
Processed Recipe: { Name: 'Grave Digger', Ingredients: [ { Text: 'vanilla-infused brandy*', Type: 'Alcohol', Liquor_type: 'Brandy', Name_brand: null, Unit_of_measure: 'ounces', Measurement_or_unit_count: '1 1/2' }, { Text: 'espresso, freshly brewed', Type: 'Bittering_agent', Liquor_type: null, Name_brand: null, Unit_of_measure: 'ounces', Measurement_or_unit_count: '1' } ], Steps: [ { Number: 1, Instruction: 'Add all ingredients into a shaker with ice and shake until well-chilled.' }, { Number: 2, Instruction: 'Strain into a coupe.' }, { Number: 3, Instruction: 'Top with whipped cream and crushed Oreo cookies (discarding cream in center).' } ], Garnishes: [ 'whipped cream', 'oreo cookies, crushed' ] }

So, yeah, the main issue being that it dropped some ingredients that were present in the original LLM reply. Separately, the original LLM Reply misclassified the `Type` field in `coffee liqueur`, which should have been `Alcohol`.

hellovai
appreciate you tyring it. the reason it dropped the day was due to your type system not being understood by the LLM you're using.

the model replied with

       {
          "Text": "coffee liqueur",
          "Type": "Liqueur",
          "Liquor_type": "Liqueur",
          "Name_brand": null,
          "Unit_of_measure": "ounce",
          "Measurement_or_unit_count": "3/4"
        },
but you expected a { Text: string, Type: IngredientType, Liquor_type: LiquorType or null, Name_brand: string or null, Unit_of_measure: string, Measurement_or_unit_count: string, }

there's no way to cast `Liqueur` -> `IngredientType`. but since the the data model is a `Ingredient[]` we attempted to give you as many ingredients as possible.

The model itself being wrong isn't something we can do much about. that depends on 2 things (the capabilities of the model, and the prompt you pass in).

If you wanted to capture all of the items with more rigor you could write it in this way:

    class Recipe {
        name string
        ingredients Ingredient[]
        num_ingredients int
        ...

        // add a constraint on the type
        @@assert(counts_match, {{ this.ingredients|length == this.num_ingredients }})
    }
And then if you want to be very wild, put this in your prompt:

   {{ ctx.output_format }}
   No quotes around strings
And it'll do some cool stuff
hellovai
if you share your prompt with me on promptfiddle.com i can play around with it and see how i can make it better!

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