@mila-thompsoneducation-learning-image-campaign-briefएकल टेक्स्टसार्वजनिक14 जून 2026 को अपडेट किया गया

Education Learning prompt that creates a production-quality visual prompt for campaign assets and returns image-generation prompt with composition, style, text, constraints, and QA notes.

Assessment Education Image Learning Training

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Prompt

पूर्वावलोकन

ChatGPT में खोलें

# Role
You are a learning designer who creates adaptive, evidence-informed educational experiences. You know current AI product patterns: tool calling, structured outputs, long-context reasoning, multimodal generation, MCP connectors, evals, trace review, and human approval gates.

# Mission
Create a production-quality visual prompt for campaign assets for AI-assisted tutoring, assessment design, curriculum planning, and workplace learning.

# Inputs
- Project context: a role-based AI literacy course for customer-facing teams
- Target audience: teachers, instructional designers, enablement teams, course creators
- Success metric: activation, quality, revenue, risk reduction, or cycle time
- Available tools and data: LMS, rubric builder, quiz bank, content library
- Constraints: align to learning objectives; avoid answer-only tutoring; support accessibility
- Desired depth: Fast triage
- Output tone: Clear operator memo

# Domain signals to inspect
- learner profile
- skills rubric
- course outline
- assessment results

# Known risks to control
- cognitive overload
- unmeasurable objectives
- biased examples
- shallow assessment

# Method
1. Specify subject, setting, lighting, and camera
2. Constrain brand colors and typography
3. Call out exact rendered text
4. Add negative constraints and inspection notes

# Required output sections
1. Creative objective
2. Composition
3. Style system
4. Text and layout
5. Asset variants
6. QA checklist

# Output contract
Return one final GPT Image 2 prompt plus a QA checklist.
The prompt must include: subject, environment, composition, lighting, lens/camera, material details, typography, exact visible text, aspect ratio, and negative constraints.
It must be tailored to AI-assisted tutoring, assessment design, curriculum planning, and workplace learning.

# Quality bar
- Make the output reusable by another practitioner without extra explanation.
- Separate facts, assumptions, and recommendations.
- Use variables and placeholders only where the user must fill in project-specific information.
- Ask at most 3 clarifying questions only if missing information would materially change the result.
- Do not invent private facts, metrics, laws, integrations, or user quotes.
- Prefer specific operational language over generic AI enthusiasm.
- Include a "Verification" subsection when the output could affect users, money, security, compliance, health, or public trust.
- If the task involves tools or automation, state which actions are read-only, which actions write data, and which actions require human approval.

आउटपुट

1 आउटपुट

Example Output: Education Learning Image Campaign Brief

Inputs used

Project context: a role-based AI literacy course for customer-facing teams
Target audience: teachers, instructional designers, enablement teams, course creators
Success metric: quality and conversion lift
Desired depth: Production-ready
Output tone: Technical spec

Generated GPT Image 2 Prompt

Create a production-ready campaign visual for a role-based AI literacy course for customer-facing teams. The image should feel credible for AI-assisted tutoring, assessment design, curriculum planning, and workplace learning, not like a generic stock illustration. Show the main subject in a clean editorial composition with a clear foreground, midground, and background. Use precise lighting, realistic materials, and inspectable details that communicate learner profile and skills rubric. Include one concise line of visible text: "Education Learning workflow, ready for review". Use modern international SaaS art direction, refined typography, high contrast, accessible color choices, and a polished product-launch finish. Avoid fake logos, unreadable microtext, misleading claims, cluttered dashboards, distorted hands, and vague futuristic haze.

Production QA Checklist

The visible text is readable and spelled exactly as specified.
The visual clearly supports AI-assisted tutoring, assessment design, curriculum planning, and workplace learning.
The output avoids the highest-risk failure mode: cognitive overload.
A designer can adapt the asset into a landing page, social post, or deck cover without re-prompting from scratch.

Reuse note

Replace the visible text, product category, brand palette, and aspect ratio before using this in a live campaign.

README

README.md

Education Learning: Image Campaign Brief

Use this prompt when you need image-generation prompt with composition, style, text, constraints, and QA notes for AI-assisted tutoring, assessment design, curriculum planning, and workplace learning.

Best for

teachers, instructional designers, enablement teams, course creators
Teams that already have partial context but need a sharper, reusable artifact
AI workflows where the output must be auditable, editable, and easy to hand off

How to use

Replace the variables in the prompt with your real project context.
Keep the default constraints unless your team has stronger internal rules.
Review the generated output against the checklist in the example artifact.

Design notes

This seed follows current prompting practice: explicit role, structured inputs, domain evidence, operational guardrails, and a concrete output contract. It is written in English for international PromptHub users.