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Anteprima dell'infografica sulla tokenizzazione a quattro pannelli
Immagine di riferimento principale
GPT Image 2 casoGrafica e posterImmagine in immagine1 rif.

Simpatica infografica a quattro pannelli sulla tokenizzazione

Questo prompt genera un'ampia infografica disegnata a mano con due personaggi mascotte che spiegano quattro metodi di tokenizzazione, ideale per post educativi sui social media riguardanti l'IA e l'elaborazione del linguaggio naturale.

Questo è un esempio di prompt GPT Image 2 per Grafica e poster . Utilizza il prompt pronto per la copia qui sotto per generare immagini simili e verifica l'attribuzione Awesome GPT Image 2 Prompts e i diritti di utilizzo commerciale prima del riutilizzo.

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{
  "type": "hand-drawn educational infographic illustration",
  "style": "cute notebook doodle style, hand-painted handbook aesthetic, warm beige paper background with faint texture, playful sketchy outlines, soft pastel colors, small flower and star doodles, taped paper-card layout",
  "aspect_ratio": "wide horizontal",
  "subject": {
    "characters": [
      {
        "name": "{argument name=\"dog character name\" default=\"Chai Xiaoqi\"}",
        "species": "Shiba Inu mascot",
        "appearance": "cute orange-brown Shiba Inu with cream muzzle and belly, round face, upright ears, friendly expression, simplified cartoon anatomy"
      },
      {
        "name": "{argument name=\"blue character name\" default=\"Token\"}",
        "species": "blue token mascot",
        "appearance": "rounded triangular blue droplet-shaped character with glossy gradient body, tiny arms and legs, rosy cheeks, smiling face, the word \"{argument name=\"token label\" default=\"TOKEN\"}\" written across the body in white"
      }
    ],
    "theme": "compare four levels of text segmentation in a friendly science-explainer format"
  },
  "layout": {
    "sections": [
      {
        "title": "1. Word-level Segmentation",
        "position": "far left panel",
        "count": 1,
        "subtitle": "Divided according to complete semantics",
        "scene": "A Shiba Inu and a blue Token character stand next to a basket labeled \"Tokens\", which is filled with slips of paper. Visible slips and labels are written with \"Programmer\", \"Left\", \"Artificial Intelligence\", and a small sign next to it reads \"ProgrammerLeft\". The Token character points to the basket, while the Shiba Inu displays sorted fragments."
      },
      {
        "title": "2. Character-level tokenization",
        "position": "left-center panel",
        "count": 1,
        "subtitle": "Splitting the text character by character",
        "scene": "A blue Token character uses a magnifying glass to examine small character blocks scattered on the floor, while a Shiba Inu organizes them nearby. A label strip at the bottom reads \"ProgrammerLeftArtificialIntelligence\", serving as a continuous text sample broken down into individual characters."
      },
      {
        "title": "3. Subword tokenization",
        "position": "right-center panel",
        "count": 1,
        "subtitle": "Divide by root words and prefixes",
        "scene": "The Shiba Inu points to a project demonstrating root word splitting and arrows, with a blue Token mascot standing next to it. Small paper labels contain mixed fragments such as \"program\", \"member\", \"Left\", \"artificial\", \"intelligence\", emphasizing morpheme or subword groupings."
      },
      {
        "title": "4. Byte-level tokenization",
        "position": "far right panel",
        "count": 1,
        "subtitle": "Convert to byte encoding combinations for division",
        "scene": "the Shiba Inu uses a retro computer with a long printed strip emerging from it, and the blue token character stands to the side. Across the lower half are ribbon-like strips filled with repeated byte numbers and symbols, especially \"1\", \"2\", \"3\", \"4\", and \"-3\", illustrating byte encoding combinations."
      }
    ],
    "decorations": {
      "count": 10,
      "items": [
        "4 pieces of taped corners in pale yellow and pink",
        "small five-point stars scattered around panels",
        "tiny flower doodles between panels",
        "curved motion lines near character gestures",
        "notebook-style vertical panel dividers",
        "rounded rectangular panel frames",
        "light shadows behind paper panels",
        "faint horizontal paper grain",
        "small spark icons",
        "hand-drawn accent marks"
      ]
    }
  },
  "composition": "four equal vertical panels arranged left to right across one canvas, each panel with a bold numbered Chinese heading and a smaller subtitle beneath it, each panel showing the two mascots acting out a different tokenization concept",
  "text": {
    "headings": [
      "1. Word-level Segmentation",
      "2. Character-level tokenization",
      "3. Subword tokenization",
      "4. Byte-level tokenization"
    ],
    "subtitles": [
      "Divided according to complete semantics",
      "Splitting the text character by character",
      "Divide by root words and prefixes",
      "Convert to byte encoding combinations for division"
    ]
  },
  "quality": "clean, polished, adorable, presentation-ready educational visual",
  "use_case": "social media science explainer comparing tokenization methods for AI and NLP"
}

Variabili di richiesta

Segnaposto per gli argomenti modificabili presenti nel prompt, con i relativi valori predefiniti.

3
Variabile
dog character name
Predefinito
Chai Xiaoqi
Variabile
blue character name
Predefinito
Token
Variabile
token label
Predefinito
TOKEN

Best for

  • - Grafica e poster visual exploration
  • - Image generation and reference-image edits
  • - Reusable briefs that keep source attribution visible

Change these parts

  • - Subject, product, character, or scene
  • - Aspect ratio, camera, lighting, and background
  • - Brand, text, color, and output constraints

Recommended model and settings

  • - Model: gpt-image-2
  • - Input mode: Immagine in immagine
  • - Aspect ratio: source
  • - Commercial status: review original source

How to use this prompt

  1. 1. Copy the prompt and preserve its structure for the first test.
  2. 2. Replace the subject, context, and publishing channel.
  3. 3. Change one camera, lighting, or style variable per iteration.
  4. 4. Inspect the output, source, text, and rights before reuse.

Limitations, source, and reuse cautions

  • - Generated output is an editable draft, not factual, legal, or rights evidence.
  • - Review the linked source, people, brands, logos, text, and third-party media before commercial use.
Importato da Awesome GPT Image 2 Prompts . È richiesta l'attribuzione. Lo stato di utilizzo commerciale è allowed ; verificare i diritti della fonte prima dell'utilizzo a pagamento.

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Note sul riutilizzo e sulla fonte

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  1. 1.Copia il prompt oppure aprilo direttamente in Dovoo tramite il pulsante di generazione.
  2. 2.Regola le variabili, le proporzioni e le immagini di riferimento in base alle tue esigenze.
  3. 3.Prima della pubblicazione o dell'utilizzo a pagamento, verificare i diritti di fonte, i requisiti di attribuzione e i rischi relativi al marchio o all'immagine.