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

10s

Input Tokens

1072

Output Tokens

210

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": [
    "basmati rice",
    "bay leaf",
    "cardamom pods",
    "chicken",
    "cilantro",
    "cinnamon stick",
    "cloves",
    "gai",
    "garam masala",
    "ginger-garlic paste",
    "lemon",
    "mint",
    "milk",
    "oil",
    "onions",
    "plain yogurt",
    "red chili powder",
    "salt",
    "saffron",
    "turmeric"
  ]
}