Skip to content

Main content

Random Wordle Feedback Exercise Generator

Draw supplied equal-length words and guesses with duplicate-aware Wordle feedback and a separate answer key, without an official dictionary or unique-deduction claim.

Loading tool…

Description

Draw supplied equal-length words and guesses with duplicate-aware Wordle feedback and a separate answer key, without an official dictionary or unique-deduction claim.

A generated practice exercise contains a prompt and an answer key under a specific rule set. A planted answer establishes one possible solution; only an explicit enumeration or uniqueness check establishes whether alternatives exist.

Draw supplied equal-length words and guesses with duplicate-aware Wordle feedback and a separate answer key, without an official dictionary or unique-deduction claim.

When to use this tool

Use this generator to create reproducible wordle feedback exercise cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

  1. Supply answers, guesses, provenance, guessCount, hideSecret in the field shapes below. Set seed when you need an explicit replay.
  2. Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
  3. Read puzzle, answer, provenance. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Choose a secret from the distinct supplied answer bank, draw guesses with replacement and delegate duplicate-aware Wordle feedback for each round. Return the puzzle and a separate answer key, hiding the secret only in the puzzle when hideSecret is true.1

Delegated operations used for this result: wordle feedback pattern green yellow gray. Their calculation or parsing conventions apply to the corresponding result fields.2

Input fieldMeaning and unitsAccepted input
answersAnswersRequired. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 32.; minimum items 1; maximum items 128.
guessesGuessesRequired. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 32.; minimum items 1; maximum items 128.
provenanceProvenanceRequired. Text; minimum UTF-16 length 1; maximum UTF-16 length 512.
guessCountGuess CountRequired. Safe integer; minimum 1; maximum 64.
hideSecretHide SecretRequired. Boolean.
seedOptional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.Optional. Safe integer.
Output fieldInterpretationReturned shape
puzzlePuzzleRequired. Object; no unknown keys.
puzzle.answerWordAnswer WordRequired. Text; minimum UTF-16 length 1; maximum UTF-16 length 32. or Null.
puzzle.roundsRoundsRequired. Array of Object; no unknown keys.; minimum items 1; maximum items 64.
puzzle.rounds[].guessGuessRequired. Text; minimum UTF-16 length 1; maximum UTF-16 length 32.
puzzle.rounds[].feedbackFeedbackRequired. Object; no unknown keys.
puzzle.rounds[].feedback.feedbackCodesFeedback CodesRequired. Array of Safe integer.; minimum items 0; maximum items 32.
puzzle.rounds[].feedback.feedbackPatternFeedback PatternRequired. Text.
answerAnswerRequired. Object; no unknown keys.
answer.answerWordAnswer WordRequired. Text; minimum UTF-16 length 1; maximum UTF-16 length 32.
provenanceProvenanceRequired. Text; minimum UTF-16 length 1; maximum UTF-16 length 512.
randomnessReplay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source.Required. Object; no unknown keys.
randomness.seedOptional numeric replay seed; normalization and precedence are explained below.Required. Safe integer. or Null.
randomness.sourceSourceRequired. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json
{
  "input": {
    "answers": [
      "apple",
      "allee"
    ],
    "guesses": [
      "apple",
      "alley"
    ],
    "provenance": "Caller-supplied illustrative words",
    "guessCount": 2,
    "hideSecret": true,
    "seed": 42
  },
  "output": {
    "puzzle": {
      "answerWord": null,
      "rounds": [
        {
          "guess": "apple",
          "feedback": {
            "feedbackCodes": [
              2,
              0,
              0,
              1,
              2
            ],
            "feedbackPattern": "20012"
          }
        },
        {
          "guess": "alley",
          "feedback": {
            "feedbackCodes": [
              2,
              2,
              2,
              2,
              0
            ],
            "feedbackPattern": "22220"
          }
        }
      ]
    },
    "answer": {
      "answerWord": "allee"
    },
    "provenance": "Caller-supplied illustrative words",
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

  • All answers and guesses must have equal length. Words and provenance come from the caller, without an official dictionary or a guarantee that feedback uniquely determines the secret. Keep the answer object out of student-facing exports.
  • A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
  • The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

  1. Tool behavior and example: Random Wordle Feedback Exercise Generator — tool.io

  2. Calculation or validation: game/wordle-feedback-pattern-green-yellow-gray — tool.io

Similar or alternative tools