edu-image-prompt

name

edu-image-prompt

description

Generates structured 5-block image-generation prompts for educational visuals from any knowledge point. Produces copy-paste-ready prompts optimized for GPT Image 2, Nano Banana, and Midjourney with reliable Chinese text rendering. Use when the user wants to turn a concept, lesson, or topic into a knowledge card (图文卡片), infographic (知识地图), process diagram (流程图), classroom illustration (课堂配图), or social-media study card (知识卡片), or asks for a 生图 Prompt / AI 配图 / 教学素材.

edu-image-prompt

Turns any educational knowledge point into a copy-paste-ready image-generation prompt that produces clean, readable educational visuals — not decorative posters.

When to use

• Turning a concept / lesson point / outline into a visual prompt
• Creating PPT slides, knowledge cards, process diagrams, lecture handouts
• Concept visualization for abstract theories or technical terms
• Social-media education cards (Xiaohongshu, public-account posts)

When NOT to use

• User wants the image itself generated directly (not a prompt)
• User wants a photo edit or image retouching
• User wants a logo, brand asset, or marketing creative

Workflow (3 steps)

Step 1 — Style setup

If the user already specified a style ("用黏土风", "做小红书卡片"), skip to Step 2.

Otherwise, use sensible defaults and proceed — do NOT block on a menu:

Parameter	Default	Full menu (only if user asks "有哪些选项")
领域美学	科技/AI 玻璃态	A)科技 B)商学黑金 C)人文复古 D)少儿黏土
质感渲染	扁平 UI	A)等距3D B)扁平UI C)场景实拍+AR
展示平台	通用讲义 16:9	A)PPT/直播 B)小红书3:4 C)通用16:9
目标模型	GPT Image 2	A)GPT Image 2 B)Nano Banana C)Midjourney D)不指定

Rationale: most users just want a good card fast. Give them the default, let them override.

Step 2 — Knowledge decomposition (CRITICAL)

Fill this template. Do NOT skip — this is the step that separates a useful educational image from a pretty-but-empty poster.

Decomposition table (copy and fill):

#	Visual goal (must be drawable)	Module	On-image text (≤8 chars)
1	[具体可绘制元素, e.g. "一句话被拆成积木块"]	analogy / definition / ...	"≤8字"
2	...	...	...
3	...	...	...
4	(optional) ...	...	...

Module pool (pick 5-8, see type-guide.md for details): definition · example · steps · comparison · pitfall · analogy · gold-sentence · rating · flow-arrow · zoom-detail · warning · mnemonic

Validation checkpoint — before proceeding, verify:

• Every goal is drawable (if any reads like "understand X", rewrite it more specifically)
• 5-8 modules selected (fewer = empty, more = labels get dropped)
• At least one pitfall or warning module (makes the image feel authored)
• If the topic is abstract, an analogy module is included

Step 3 — Generate, validate, and deliver

1. Assemble the 5-block prompt using the template in prompt-rules.md, applying the style from Step 1 and modules from Step 2.
2. Apply model-specific techniques from model-guide.md (e.g., GPT Image 2 thinking mode, MJ --ar parameter).
3. Quick-check against the validation rules below.
4. Deliver following the output format (prompt first, design notes after).

The 5-block prompt structure

Every prompt must follow this order (see prompt-rules.md for full template):

[1] CANVAS       — dimensions, background, dominant accent color
[2] HEADER       — exact title text wrapped in THE TEXT READS: "..."
[3] CONTENT BLOCKS — 5-8 named modules with labels
[4] VISUAL       — line weight, icon style, arrow style, palette
[5] CONSTRAINTS  — text accuracy rules + negative keywords

The #1 text-rendering rule: wrap every Chinese string in THE TEXT READS: "...". This is the biggest accuracy lever for CJK text (source: OpenAI cookbook, validated across 300+ generations).

Output format

Deliver in this order (prompt first — that's what the user copies):

1. 📋 The Prompt — the complete 5-block prompt in a code block, copy-paste ready
2. Design notes (brief) — recommended type, decomposition summary, model hint, variants

See output-format.md for the exact template.

Quick validation rules

Before delivering, verify:

• Prompt follows 5-block order (CANVAS first)
• Every CJK label wrapped in THE TEXT READS: "..."
• Total on-image Chinese text ≤ 60 characters
• Font hierarchy specified (≥ 3 tiers)
• Negative constraints included (no garbled text, no Lorem ipsum, no watermarks)
• If target is Midjourney: no Chinese text on image

Full checklist: validation.md. Pitfall fixes: troubleshooting.md.

References (one level deep — read when needed)

• prompt-rules.md — 5-block template + text rendering rules
• model-guide.md — GPT Image 2 / Nano Banana / MJ v7 adaptation
• type-guide.md — 8 preset types + 12-module pool
• style-config.md — domain aesthetics + color palettes
• examples.md — 3 complete worked examples
• output-format.md — output structure template
• validation.md — full quality checklist
• troubleshooting.md — 6 common pitfalls and fixes