Below is a prompt to give the Claude AI when creating a conceptual mind map (or conceptual mind maps) for a blog article:
Read the entire article carefully. Create the minimum number of SVG mind maps
necessary to represent the article's conceptual structure. Prefer one map; use
multiple only if the article contains genuinely distinct conceptual systems that
would overlap or become illegible in a single diagram.
CONTENT RULES:
- Represent underlying knowledge structure, not paragraph summaries
- Preserve causal/influence relationships, distinctions, and mechanisms
- Remove minor examples, repetition, incidental detail
- Do not add unsupported information
- After drafting, re-check the map against the article and correct any omitted
or distorted concept, relationship, or qualification before finalizing
VISUAL / LAYOUT RULES:
- Output raw SVG only — no HTML wrapper, no
<img>, no external libraries, no JS - Use a radial or left-to-right hierarchical tree layout (specify which)
- Set viewBox explicitly (e.g. "0 0 1600 1000") and make width/height 100% so
it scales responsively inside Blogger's fixed-width post column - Central/root node in the center or far left; branch outward by hierarchy level
- Cap hierarchy at 3 levels deep (root → main branch → sub-node) — collapse
anything deeper into the sub-node's label rather than adding a 4th tier - Limit to ~6-8 main branches and ~4-5 sub-nodes per branch max, to prevent
visual clutter and node overlap - Wrap node text at a fixed character width (e.g. ~28 chars/line) using
multiple<tspan>lines rather than letting text overflow its shape - Give every node a background shape (rounded rect or ellipse) sized to fit
its wrapped text, not fixed-size boxes with overflowing text - Use a consistent color per hierarchy level (root, branch, sub-node) — 3-4
colors max, applied via inline fill/stroke attributes (no CSS classes,
since Blogger strips<style>blocks unpredictably) - Render causal/influence relationships as directional arrows (
<marker>or
manually drawn arrowhead paths) with a short inline label if needed
(e.g. "shapes", "→ reinforces →") - Ensure no text or arrow crosses over another node — check coordinates
before finalizing, don't estimate - Use a readable sans-serif font-family fallback stack (e.g. Arial, Helvetica,
sans-serif) since custom fonts won't load in Blogger - Keep a consistent stroke-width and font-size scale across all nodes at the
same hierarchy level for visual consistency
TEXT-FIT RULES:
- Size every node's background shape from the actual measured width of its text at
the target font/weight/size — not a character-count formula. Use a real font
metrics source (e.g. a metric-compatible font like Liberation Sans Bold for
Arial Bold, measured via a library such as PIL/Pillow, or an equivalent text-measurement
tool) to get each line's true rendered width, then add ~15-20% horizontal
padding as a safety margin. - Do not use the
textLength/lengthAdjust="spacingAndGlyphs"attribute. It forces
the renderer to stretch or compress a line's glyphs to exactly match the given
width — if every line in a multi-line node is given the same textLength (e.g. the
node's overall inner width), short lines get their letters stretched wide to fill
it, producing visibly oversized, distorted words. If textLength is used at all, it
must be computed per-line from that line's own measured width, never shared across
lines in the same node — but given accurate width measurement above, it is not
needed and should simply be omitted. - Vertical padding: node height must accommodate all wrapped lines at
1.3× font-size line-height, plus at least 16px total top/bottom padding.
READABILITY / SCALE RULES:
- Design the canvas assuming final display width will be ~600px on desktop
and as narrow as ~380px on mobile — Blogger's column, not a full monitor - Set viewBox width no larger than ~1000-1100px for a single-column diagram.
If the content requires more room than that to stay legible, split into
additional diagrams rather than widening the canvas further - Minimum font sizes in source units (before any shrinking):
root/title nodes ≥ 30px, branch/category nodes ≥ 24px, leaf/sub-nodes ≥ 20px,
annotation/caveat text ≥ 17px — never smaller, even under space pressure - If node count forces text below these minimums to avoid overlap, that's a
signal to remove a node, shorten its label, or split into another diagram —
not to shrink the font - Cap total nodes per diagram more conservatively: ~4-5 main branches with
2-3 sub-nodes each (12-18 nodes max), rather than 6 branches x 3 subs (18-24).
Wide hexagonal/radial spreads eat horizontal space that mobile doesn't have —
prefer a vertical or top-to-bottom layout over a wide radial one when the
concept allows it
VERIFICATION STEP (mandatory, not optional):
- Before delivering, rasterize the SVG to a PNG at both full size and a 400px-wide
mobile simulation — do not rely on mental estimation of whether text fits. - Use a renderer that honors the same SVG text/attribute behavior as a real browser
(e.g.rsvg-convert/librsvg, or an actual headless browser). Do not rely solely on
cairosvg for this check — it silently ignores some attributes (including
textLength) that browsers apply, so a clean cairosvg render does not guarantee a
clean render on the actual publishing platform. - Visually inspect the rendered PNG and confirm: no character in any node
touches or crosses its shape's edge, no line's text is stretched or compressed
relative to other lines in the same node, no text or arrow crosses over another
node, and every label is legible at 400px width. - If any overflow, overlap, or distortion is found, widen the affected node, shorten
the label, or adjust spacing, then re-render and re-check before finalizing.
Repeat until the rendered image is clean — do not deliver on the first pass
without this visual check.
DELIVERY:
- Output must be a single self-contained
<svg>...</svg>block, valid XML,
ready to paste directly into Blogger's HTML view with no further editing - If more than one map is genuinely needed, output each as a separate
complete<svg>block with a plain-text heading above it (not inside the SVG)
Questions to ask other AIs (ChatGPT and Gemini) if Claude is not available
Using Conceptual Mind Maps to Learn This Blog’s Article Series on Beliefs
For this blog’s 32-article series, The Psychology of Belief: 32 Part Series – How Your Inner World Shapes Your Outer Life, I think conceptual mind maps are particularly useful.
My Recommendation
For this 32-article series, I would use them in three stages.
Stage 1 — Now:
Create an AI-generated conceptual mind map beneath each article.
Stage 2 — After the series is mature:
Create a master conceptual map connecting the major concepts across the articles.
Stage 3 — Learning layer:
Use the maps to generate retrieval questions or flashcards for the most important concepts and relationships.
That gives you:
Articles → Individual conceptual mind maps → Master conceptual map → Retrieval practice
That is a remarkably efficient way of turning a collection of articles into an actual structured learning system rather than simply a collection of web pages.
I Would Actually Use Two Levels of Conceptual Maps
1. A Conceptual Mind Map for Each Individual Article
Put it directly below the article, exactly as proposed.
For example, article #13, How Beliefs Are Formed, could have a map organized around:
Belief Formation
- Quality of thinking
- Repetition
- Emotion
- Authority
- Experience
- Interaction among the five forces
- Implications for changing beliefs
The purpose is not to reproduce the article in diagram form. The purpose is to give the reader a compact representation of its conceptual structure.
A good map should help answer questions such as:
- What are the major concepts?
- How are they related?
- What causes or influences what?
- What distinctions does the article make?
- What mechanisms does it describe?
- What practical conclusions follow from those relationships?
2. Eventually, One Master Conceptual Map for the Entire Series
This is where I think the project becomes particularly interesting.
The 32 articles are not simply a collection of unrelated topics. They form a broader conceptual progression.
What beliefs ARE
→ definition
→ layers
→ beliefs vs. truth vs. knowledge
How beliefs FORM
→ quality of thinking
→ repetition
→ emotion
→ authority
→ experience
How beliefs OPERATE
→ mindset
→ interpretation
→ behavior
→ habits
→ self-fulfilling loops
How beliefs CHANGE
→ evidence
→ updating
→ identity
→ CBT
→ cognitive restructuring
How beliefs INTERACT WITH LIFE
→ emotional regulation
→ assumptions
→ social transmission
→ performance
→ work and self-employment
That is essentially a conceptual architecture of belief.
The individual maps would represent the architecture of each article. The master map would represent the architecture of the entire series.
Why the Two-Level Approach Is Useful
The individual maps and the master map serve different purposes.
The individual map answers:
“What are the important concepts in this article, and how do they relate?”
The master map answers:
“How do the ideas from all of these articles fit together into one larger system?”
This distinction becomes especially valuable as the number of articles increases.
A reader who encounters article #20 might otherwise have to remember what articles #3, #8, #13, and #17 were about. The master map can provide a higher-level representation showing where the current article fits within the larger intellectual structure.
In other words, the master map can give readers a mental framework into which new information can be placed.
The Learning Layer
The maps can also become the foundation for a more powerful learning system.
Instead of turning every sentence in an article into a flashcard, important concepts and relationships from the conceptual maps can be converted into retrieval questions.
For example:
Concept map:
Emotion → influences → belief formation
could generate a retrieval question such as:
“How can emotion influence belief formation?”
Similarly:
Belief → influences → interpretation → influences → behavior
could generate:
“Explain the pathway through which beliefs can influence behavior.”
This creates a useful progression:
Article
↓
Conceptual mind map
↓
Important concepts and relationships
↓
Retrieval questions
↓
Spaced retrieval
The map therefore isn't replacing a flashcard system. It is helping determine what is worth remembering and how the pieces fit together.
Eventually, the Maps Can Become a Knowledge Architecture
There is another potential advantage to building the maps incrementally.
As more articles are mapped, recurring concepts will become visible.
You may discover that a concept introduced in an early article becomes a mechanism in a later article, that two apparently different topics are connected, or that several articles are actually examining different aspects of the same underlying process.
The master map can therefore evolve as the series develops.
Instead of thinking of the blog as:
32 separate articles
you can eventually represent it as:
One interconnected conceptual system expressed through 32 articles.
That is a much more powerful structure for learning.
What I Would Have the AI Do
I would not simply tell the AI:
“Make a mind map of this article.”
That instruction is too vague. The resulting diagram might simply reproduce the article's headings or produce a visually attractive summary without accurately representing the relationships among the ideas.
The more detailed prompt at the beginning of this article is better because it tells the AI what the map is actually supposed to accomplish.
In particular, the AI should:
- read the entire article;
- identify its underlying conceptual structure;
- identify important relationships among concepts;
- preserve important distinctions and qualifications;
- remove minor examples and repetition;
- avoid unnecessary nodes;
- compare the completed map with the original article;
- correct omissions or distortions; and
- avoid introducing information that is not supported by the article.
The goal is not to make the largest or most elaborate diagram possible.
The goal is to make the smallest map that accurately represents the important conceptual structure.
Conceptual Mind Maps Rather Than Formal Concept Maps
There is an important terminology issue worth clarifying.
A traditional mind map is generally a hierarchical diagram organized around a central topic. A formal concept map places greater emphasis on explicit relationships among concepts, often using labeled connections and cross-links.
The maps described in this article occupy something of a middle ground.
They use the hierarchical structure and software commonly associated with mind mapping, but their purpose is more conceptual: representing the important ideas and relationships in an article.
For that reason, conceptual mind map is a useful description of the approach.
The terminology is less important than the function. The objective is to transform a linear article into a visual representation of its knowledge structure.
The Overall System
For this 32-article series, I would therefore use the following architecture:
32 Articles
↓
32 Individual Conceptual Mind Maps
↓
1 Master Conceptual Map
↓
Important Concepts and Relationships
↓
RemNote/Anki Retrieval Questions
↓
Spaced Retrieval
↓
Application and Integration
Each layer has a different purpose.
The articles provide detailed explanations.
The individual maps provide compact conceptual structures.
The master map shows how the articles fit together.
RemNote and Anki provide retrieval practice and spaced review of important knowledge.
Application tests whether the knowledge can actually be used.
Bottom Line
I think adding conceptual mind maps beneath the articles is a worthwhile improvement to the series, particularly because the articles form a larger conceptual system rather than being completely independent pieces.
The most efficient implementation is to build the individual maps now, rather than waiting until the entire series is finished. Once enough articles have been mapped, create the master conceptual map that connects the major ideas across the series.
Then use that conceptual structure to create retrieval questions and integrate the material into a spaced-repetition system.
The resulting architecture is:
Articles → Conceptual mind maps → Master conceptual architecture → Retrieval practice → Application
That turns a collection of blog articles into something considerably more useful: a structured, interconnected learning system.
No comments:
Post a Comment