Data Normalization

Data processing and normalization tasks evaluate structured output from messy prose and different structures. This capability is essential for catalogue and product pipelines where data needs to be extracted from unstructured text and formatted consistently. Models must understand the desired output format and extract relevant information accurately.

Duration

Input Tokens

1187

Output Tokens

191

Cost

$0.00

Context

Input

Return a list of all ingredients part of this recipe, ordered alphabetically. Only ingredients , not quantities

Expected output

{
  "ingredients": [
    "basmati rice",
    "bay leaf",
    "chicken",
    "cilantro",
    "cinnamon",
    "cloves",
    "garam masala",
    "ghee",
    "ginger-garlic paste",
    "green cardamom",
    "lemon",
    "milk",
    "mint",
    "oil",
    "onions",
    "red chili powder",
    "saffron",
    "salt",
    "turmeric",
    "water",
    "yogurt"
  ]
}

Model output

{
  "ingredients": [
    "bay leaf",
    "boneless chicken",
    "bone-in chicken",
    "cardamom pods",
    "cilantro",
    "cloves",
    "cinnamon stick",
    "garam masala",
    "garlic",
    "ginger",
    "ghee",
    "green cardamom pods",
    "lemon",
    "marinated chicken",
    "milk",
    "mint",
    "onions",
    "plain yogurt",
    "rice",
    "saffron",
    "salt",
    "saffron and turmeric mixed milk",
    "turmeric"
  ]
}