GPT Image 2 Knowledge Graph Prompt Guide: 5 Production Templates for Exam Prep, Xiaohongshu, Lecture Notes, Slides & SOPs

Before GPT Image 2, "use AI to make a knowledge graph image" was a meme more than a workflow. Every other model would either spell the headings wrong, jam ten sections into three, or output a pretty-but-empty poster that read like decorative noise. So most people gave up and went back to PowerPoint, Figma, or hand-drawn iPad notes.

That changed this spring. With GPT Image 2, you can now hand the model a single structured prompt and get back a study-grade knowledge card — correct typography, clean section grid, the right arrows in the right places — in one shot. This guide is the prompt framework I keep reusing, plus five copy-paste templates for the five scenarios where readers actually search for this stuff: civil-service exam prep, Xiaohongshu / social knowledge cards, classroom handouts, slide visuals, and operational SOPs.

1. First, what does "AI knowledge graph image" even mean?

Two very different things share that phrase:

• Technical knowledge graph (KG): an entity–relation–entity triple store, the kind you build with Neo4j / RDF / GraphRAG. That's a data structure, not an image.
• Visual knowledge-graph image: a printable / shareable poster that visually organises a topic — definition, types, workflow, pitfalls, examples, mnemonic. This is what people actually want when they search "how to make a knowledge graph with AI."

This guide is about the second one. The first is an LLM + graph-database job and has nothing to do with image models — it's out of scope here.

2. The 5-block universal prompt template

Every successful knowledge-graph image I've shipped with GPT Image 2 follows the same five blocks, in this order. Skip any one and the output collapses into either pretty wallpaper or alphabet soup.

[1] CANVAS — orientation (vertical 1024×1536 or horizontal 1536×1024),
    paper-style background, dominant accent color.
[2] TOPIC HEADER — exact title text + one-line positioning sentence.
[3] SECTIONS — 5 to 8 named cards. Common skeleton:
    Definition → Question Types → Solving Path → Common Pitfalls →
    Comparisons → Worked Example → Mnemonic.
[4] VISUAL — line weight, icon style, arrow style, palette accents,
    rounded-card vs. ruled-notebook aesthetic.
[5] CONSTRAINTS — "THE TEXT READS: ..." quoting for every label,
    no extra logos, no decorative filler, preserve exact spelling.

Why these five and not six or four:

• Canvas first. The model commits to layout before content. If you describe the topic before the canvas, you get random aspect ratios.
• Header before sections. The H1 anchors hierarchy. Without it, all section cards render at the same visual weight and the image reads flat.
• 5–8 sections is the sweet spot. Below five looks empty; above eight starts losing labels even on GPT Image 2.
• Visual block last among the descriptive ones. Style descriptions placed early get over-applied; placed late they act as a finishing pass.
• Constraints at the bottom, in imperative voice — the model treats them as guardrails rather than suggestions.

One trick worth repeating from the OpenAI cookbook: every piece of on-image copy should be wrapped as THE TEXT READS: "...". That single phrase is the biggest accuracy lever for non-Latin scripts and long titles.

3. Scenario 1 — Civil-service exam study card (the headline example)

This is the one I'd start with, because exam-prep knowledge cards are the strictest test: they need accurate Chinese, dense information, a clear solving workflow, and a "study-handout" aesthetic that doesn't look AI-generated.

Here's the prompt that produced the cover image of this post (a study card for "Civil-Service Interview Analysis Question Framework"):

Create a vertical (1024×1536) civil-service exam study infographic
on the topic "面试综合分析题答题结构 / Civil-Service Interview
Analysis Question Framework". The goal is to help a reader
understand: what this question type tests, how to recognise it,
the standard answering workflow, common pitfalls, similar
question types, a worked example, and a memorable mnemonic.

CANVAS: clean light paper background, deep-navy title block,
charcoal body lines, with restrained accents in blue, teal, gold,
and a single red flag for warnings.

LAYOUT BLOCKS (rounded cards with thin borders, numbered tags):
1) Header: exact title + one-line positioning.
2) Core Definition — what this question actually tests.
3) Question-Type Signals — prompt phrasings and keyword tells.
4) Solving Path — 4 to 5 numbered steps connected by hand-drawn arrows.
5) Common Pitfalls — trap options, wrong reasoning patterns.
6) Comparison — distinguish from adjacent question types.
7) Worked Example — short stimulus + correct decomposition.
8) Mnemonic — one short rhyming line or three-keyword summary.

VISUAL: looks like a high-quality printed exam handout crossed
with a hand-illustrated education poster. Use rounded cards,
thin rule lines, numbered tags, hand-drawn arrows, small
zoom-in callouts, and a dedicated "易错提醒" warning strip.

CONSTRAINTS: every visible heading and body string is rendered
verbatim — THE TEXT READS exactly what is specified. No extra
logos, no Lorem ipsum, no decorative filler text. Information
density is high but the page stays balanced and uncluttered.

A few things worth noting:

• I write the Chinese title plus an English gloss. GPT Image 2 handles either, but bilingual prompts make the model treat the topic as serious / educational rather than meme-y.
• Numbered tags + hand-drawn arrows make the output read like a study handout, not a marketing flyer.
• The "easy-mistake / pitfalls" card is the single most underused element in AI knowledge cards. Adding it is what makes the image feel authored rather than generated.

You can open this exact civil-service study-card prompt in the library, then browse 20+ related templates from the same page.

4. Scenario 2 — Xiaohongshu / social-media knowledge card

For Chinese social platforms (especially Xiaohongshu / RED), the de-facto spec is 1080×1440 vertical, F-shaped reading flow, three-card series. The prompt below produces a single card from that series; reuse it three times with the same accent palette to get a swipe-through set.

Create a vertical 1080×1440 knowledge card for Xiaohongshu titled
"3 Habits That Actually Lower Cortisol".

CANVAS: warm cream background (#FFF8EE), subtle paper grain.
A single accent color of muted terracotta (#C96E5A).

LAYOUT:
- Top: progress label "认知觉醒 · 卡片 2 / 3" in tiny mono caps.
- Headline block: H1 in handwritten serif, sub-line in clean sans.
- Middle: three numbered "habit" rows. Each row has a small flat
  icon on the left, a 3-word habit name, and a 1-sentence "why".
- Bottom: a single quote strip with a hand-drawn underline.
- Right edge: tiny vertical text "swipe →".

VISUAL: minimal flat illustration, two-color palette only, 1pt
hairlines, slight paper texture, generous whitespace, F-shaped
reading flow (title top-left, scan down).

CONSTRAINTS: text rendering must be pixel-perfect — THE TEXT
READS each label exactly as written. No watermark. No platform
logos. Output looks like a published Xiaohongshu carousel card,
not a Canva template.

Two retention tricks specific to Xiaohongshu:

• The "X / Y" progress label tells viewers there are more cards — it raises swipe-through and average view duration, both of which the algorithm rewards.
• A handwritten H1 paired with clean sans body is the visual signature of "thoughtful creator" rather than "agency content," which is the entire pitch of the platform.

5. Scenario 3 — Classroom handout / lecture note

For teachers and tutors. The trick is that a handout is wider than a knowledge card — it goes horizontal, has a left navigation column, and lets the body breathe.

Create a horizontal 1536×1024 classroom handout titled
"Photosynthesis — One-Page Lecture Note for 8th-grade Biology".

CANVAS: graph-paper background, navy header bar, hand-drawn
margin notes in pencil-grey.

LAYOUT:
- Left rail (20% width): table of contents with 5 numbered items.
- Main area (80% width) split into three rows:
  Row A — annotated diagram of a leaf cross-section with arrows
    pointing to chloroplast, stomata, xylem, phloem.
  Row B — the balanced chemical equation in a boxed callout,
    with sun, water, CO2, glucose, O2 labeled as small icons.
  Row C — a 4-step "Light vs Dark Reactions" comparison table.
- Bottom-right: a "Common Misconceptions" red flag box with
  three bullets, each one sentence.

VISUAL: pen-and-marker textbook illustration style. Pencil-grey
linework, navy headings, accent yellow for highlights, red for
warnings only. Friendly but not childish.

CONSTRAINTS: every label is rendered verbatim. The handout reads
like it was prepared by a science teacher, not designed by a
marketing agency. No school logos, no watermarks.

If you're producing a whole curriculum, generate one handout, then in follow-up turns ask GPT Image 2 to preserve the layout and only swap the content block — its local-edit fidelity is what makes batch production economically viable now.

6. Scenario 4 — Slide-deck visual (boardroom-grade)

The classic painful slide elements — TAM/SAM/SOM concentric circles, 2×2 matrices, swim-lane diagrams — used to take 30 minutes each in Figma. They take one prompt now.

Create a horizontal 16:9 slide visual titled
"2026 GTM Plan — Where We Win This Quarter".

CANVAS: pure white background, single brand-blue accent (#2563EB),
charcoal text. Boardroom presentation aesthetic.

LAYOUT: 2x2 matrix.
- X axis label: "Effort (low ← → high)"
- Y axis label: "Strategic value (low ← → high)"
- Quadrants, each with a 2-word verdict tag and 2 example bullets:
  Top-right "DO NOW": Enterprise pilot, Channel partner.
  Top-left "QUICK WINS": Lifecycle email, Pricing test.
  Bottom-right "RECONSIDER": Conference circuit, New SDR hire.
  Bottom-left "PARK": Affinity merch, Side-project blog.
- Footer micro-text: "Source: Q2 OKR review, internal".

VISUAL: clean sans-serif, 1pt rule lines, generous whitespace,
no decorative icons. Reads as a McKinsey-style consulting slide.

CONSTRAINTS: every quadrant label is rendered verbatim. No
watermarks. No fake company logos. The image looks like a
PowerPoint screenshot, not a stock illustration.

Pair this prompt with a follow-up like "Now generate a TAM / SAM / SOM concentric-circle visual in the same visual language" and you have a coherent deck instead of a Frankenstein of screenshots.

7. Scenario 5 — SOP / workflow diagram

Operations teams have the cleanest ROI on this entire workflow — one good SOP diagram saves an hour of Visio time and reads better than the manual it replaces.

Create a vertical 1024×1536 SOP diagram titled
"Customer Refund Process — Standard Workflow (2026)".

CANVAS: white background, navy header strip with the SOP title
and a small "Doc ID: OPS-014 · v2.1" badge.

LAYOUT: top-to-bottom flowchart with 6 numbered nodes connected
by directional arrows.
1) "Customer submits refund request" — owner: Support L1.
2) "Verify order + payment status" — owner: Support L1.
3) Decision diamond: "Within 30-day window?" — Yes → step 4,
   No → step 6.
4) "Approve refund in Stripe dashboard" — owner: Finance.
5) "Send confirmation email + close ticket" — owner: Support L1.
6) "Escalate to Support L2 for exception review" — owner: L2.
Right side: a sidebar with three "Common pitfalls" warnings
and the SLA: "Refunds processed within 24h business time".

VISUAL: clean Visio-style flowchart, rectangles for actions,
diamonds for decisions, hand-drawn-feel arrows in navy, owner
tags in small pill labels. No icons, no illustration.

CONSTRAINTS: every node label and owner tag rendered verbatim.
No watermarks. Output reads like a print-ready company SOP page.

A subtle pro move: ask the model to render the document ID and version number in a small badge. Reviewers treat documents with version metadata as authoritative; it's the cheapest credibility upgrade in this entire workflow.

8. Six common pitfalls (and how to fix them)

After ~300 knowledge-card generations, these are the failure patterns I now pre-empt in every prompt:

If you're getting one of these and it's not on this list, open the prompt library and reverse-engineer the closest working example — that's usually faster than debugging from scratch.

9. Sizing cheat sheet

For people who only want the dimensions and a one-line prompt suffix:

The aspect ratio alone changes whether the model commits to a "card" or a "page" mental model. Use the column literally — don't translate 1024×1536 into "vertical infographic" and expect the same crispness.

The bottom line

The interesting shift isn't that GPT Image 2 "looks better." It's that one-shot, ready-to-publish knowledge images are now a workflow rather than a fantasy. The five scenarios above cover the bulk of what people actually search for when they Google "how to make a knowledge graph with AI" — and the same 5-block prompt template carries across all of them.

If you want to skip writing prompts and just see what GPT Image 2 can do, the /prompts page has 20+ production-ready templates (including the civil-service card from this post) you can fork, and /explore has the full output gallery with source prompts attached.

Further reading

• Did GPT Image 2 Really Dethrone Nano Banana? My Verdict
• GPT Image 2 vs Nano Banana 2 vs Midjourney v7 — A Production-Focused Comparison
• GPT Image 2 Prompt Writing Guide — 7 Rules for 90% Hit Rate