Únete gratis

Audit a chart for misleading design

Autor: AILesson7 min de preparaciónProbado con:ChatGPTRevisado el: 2026-08-28

Respuesta rápida

Review scale, encoding, labels, missing context, and the claim against source values. Proporciona: Chart description, Source values and definitions, Audience and intended claim. Resultado esperado: A severity-ranked chart audit and a corrected design specification.

1

Añade tu contexto

Tu texto permanece en este navegador. AILesson Prompts no lo envía a un modelo ni a un servidor.

2

Tu prompt

Los campos sin rellenar permanecen visibles como marcadores de posición, para que puedas copiar y editar el prompt

Audit this chart for misleading or unclear design.

Chart type, axes, scales, labels, colors, annotations, and title:
[chart]

Source values, definitions, units, denominators, periods, and missing data:
[source]

Audience and intended claim:
[claim]

Compare every visual encoding and textual claim with the source. Check truncated or unequal axes, area/volume distortion, dual axes, category order, omitted baselines, cherry-picked periods, changing denominators, missing uncertainty or sample size, color accessibility, and title wording. Rank findings as blocking, material, or minor. Then specify a corrected chart and state the strongest claim the evidence actually supports. Do not invent unseen source details.
Probar en Playground
Privado de forma predeterminadaLa preparación del prompt se realiza localmente en tu navegador. Evita introducir información confidencial en cualquier servicio de IA, a menos que tu organización lo permita.

De la entrada al resultado

Un ejemplo completo

Mira cómo un contexto concreto convierte esta receta en un resultado que puedes usar

Entrada real

Chart description
A 3D column chart titled 'New training doubles productivity'. It shows Before=82 and After=91 tasks per week. The y-axis begins at 80. Before is gray and After bright green. No sample size or dates are shown.
Source values and definitions
Same 12 employees measured for four weeks before and four weeks after training. Values are group means. No control group. Two employees were on leave during one after week. Individual variation and uncertainty are not supplied.
Audience and intended claim
Management slide intended to justify rolling the training out company-wide.

Ejemplo de salida

Blocking findings

The title is false: 91 is 11.0% above 82, not double. The y-axis starting at 80 makes a nine-task increase occupy almost the full chart height, and 3D perspective adds further visual distortion. Without a control group, the before-after difference cannot be attributed to training.

Material findings

The chart omits n=12, the four-week windows, paired measurement, leave-related coverage difference, and uncertainty. A mean alone hides whether improvement was widespread or driven by a few employees. Bright green also implies approval rather than neutral comparison.

Corrected specification

Use a paired dot plot of each employee's before and after mean if individual data is available, plus a clearly labeled group summary. Otherwise use a simple two-point slope or column chart with a zero baseline, flat 2D design, exact labels 82 and 91, and subtitle “Observed mean increased by 9 tasks/week (11%); n=12; uncontrolled before-after comparison.” Add the date windows, coverage note, and uncertainty once calculated. The strongest supported claim is that the observed mean was higher after training; the data does not establish that training caused the increase or predict company-wide results.

Por qué funciona

  1. 1

    Checking the visual against source values catches distortion that a style-only review misses.

  2. 2

    Severity ranking separates truth-changing defects from ordinary polish.

Comprueba el resultado

  • Are visual distances proportional to the underlying values?

  • Does the title describe evidence rather than imply unsupported causality?

  • Are denominators, periods, exclusions, and sample sizes visible where needed?

Evidencia que respalda el método

Fuentes y citas

Fuentes primarias que respaldan decisiones de diseño específicas de esta receta. No garantizan un resultado concreto de la IA.

  1. The audit checks chart clutter, readable labels, text alternatives, and linked data sources because official UK guidance identifies these as core chart accessibility and transparency practices.

    Accessible charts: a checklist of the basics — UK Government Analysis FunctionFuente revisada el:

Úsalo con confianza

Preguntas frecuentes

Respuestas prácticas sobre cuándo usar esta receta, qué debes proporcionar y en qué casos la revisión humana sigue siendo importante

What should I prepare before using “Audit a chart for misleading design”?

For “Audit a chart for misleading design,” prepare Chart description, Source values and definitions, and Audience and intended claim. Replace placeholders only with information you can verify. If a detail is unknown, preserve that uncertainty explicitly instead of asking the model to infer it.

When is the “Audit a chart for misleading design” result not ready to use?

The result is not ready if it does not yet deliver the stated outcome—A severity-ranked chart audit and a corrected design specification—from the supplied evidence, or if it relies on unresolved assumptions, missing approvals, or invented details. Use the checks as release gates: revise the source inputs or assign a named, authorized reviewer instead of polishing an unsupported output.

Which AI tools have recorded tests for “Audit a chart for misleading design”?

The published test record for “Audit a chart for misleading design” lists ChatGPT as of 2026-08-28. This confirms recorded runs, not guaranteed compatibility or identical results in later product versions. For another tool or version, keep every constraint visible and repeat the result checks before use.

Más formas de explorar

Dónde encaja esta receta

Indicaciones de textoAnálisis de datosRetroalimentación y revisiónExcel y hojas de cálculo

Mantén el trabajo en marcha

AILesson · Cursos recomendados

Tu próximo paso: pon la IA a trabajar

Pasa de entender la IA a poner manos a la obra. Practica cómo dar instrucciones, revisar y mejorar resultados con lecciones interactivas para el trabajo y la vida cotidiana.

Explora todos los cursos
Portada de IA para principiantes

15 lecciones interactivas

IA para principiantes

Inicia una conversación útil con la IA, mejora la respuesta, comprueba lo importante y reconoce cuándo te corresponde dar el siguiente paso.

Explora este curso
Portada de Fundamentos de ChatGPT

10 lecciones interactivas

Fundamentos de ChatGPT

Aprende a usar ChatGPT mediante tres flujos de trabajo prácticos: completa una primera conversación, trabaja con un archivo y usa Projects para llevar un plan comprobado a una nueva conversación.

Explora este curso
Portada de Cómo funciona la IA generativa

7 lecciones interactivas

Cómo funciona la IA generativa

Comprende cómo los chats de IA habituales aprenden patrones del lenguaje, usan los mensajes actuales, construyen respuestas e incorporan fuentes y herramientas.

Explora este curso